Drug_discovery_ML_project/Drug_discovery_ML_project.ipynb

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      "source": [
        "# Project outline\n",
        "\n",
        "In this miniproject, a drug discovery model has been built based on the drug activity data extracted from the Chembl database. I've selected HIV-1 integrase as the target protein for drug inhibition. The reasoning behind this is that just like the currently infamous virus COVID-19, HIV is also a retrovirus, which makes this an extra topical virus to target. Furthermore, the HIV-1 integrase also has plenthora of drug bioactivity data available for it on Chembl - https://www.ebi.ac.uk/chembl/target_report_card/CHEMBL3471/.\n",
        "\n",
        "The first stage of this project encompassed acquiring drug activity data (IC50) from the Chembl database, processing the data for the exploratory data analysis and calculation of molecular descriptors, and saving the resulting dataset to a dedicated Google Drive folder. Each drug molecule was labelled active, intermediate, and inactive based on the set IC50 threshold values.\n",
        "\n",
        "In the second section, the data was used to calculate molecular macro descriptors (Lipinski descriptors) such as molecular weight, and number of hydrogen acceptors. These were used for exploratory data analysis, where, via statistical methods (Mann-Whitney U Test), an attempt to witness statistical significance based on the drug activity and molecular descriptors was made.\n",
        "\n",
        "In the third section, PaDEL was used to calculate molecular micro descriptors to obtain data with improved precision for model construction. This data was used as an input into a random forest regression algorithm which was then assessed for its accuracy of drug activity prediction.\n",
        "\n",
        "Finally, more algorithms were tested and compared based on their performance and speed of computation to provide best candidates for potential hyperparameter optimisation or web-deployment.\n",
        "\n",
        "The notebook must be ran on Google Colab as this will allow the integration of Google Drive, which is essential for the code to run correctly."
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      "cell_type": "markdown",
      "source": [
        "# Data acquisition and processing"
      ],
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      "cell_type": "code",
      "source": [
        "!pip install chembl_webresource_client"
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          "text": [
            "Requirement already satisfied: chembl_webresource_client in /usr/local/lib/python3.7/dist-packages (0.10.7)\n",
            "Requirement already satisfied: easydict in /usr/local/lib/python3.7/dist-packages (from chembl_webresource_client) (1.9)\n",
            "Requirement already satisfied: requests>=2.18.4 in /usr/local/lib/python3.7/dist-packages (from chembl_webresource_client) (2.23.0)\n",
            "Requirement already satisfied: requests-cache~=0.7.0 in /usr/local/lib/python3.7/dist-packages (from chembl_webresource_client) (0.7.5)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.7/dist-packages (from chembl_webresource_client) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.18.4->chembl_webresource_client) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.18.4->chembl_webresource_client) (2021.10.8)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.18.4->chembl_webresource_client) (2.10)\n",
            "Requirement already satisfied: itsdangerous>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from requests-cache~=0.7.0->chembl_webresource_client) (2.0.1)\n",
            "Requirement already satisfied: attrs<22.0,>=21.2 in /usr/local/lib/python3.7/dist-packages (from requests-cache~=0.7.0->chembl_webresource_client) (21.4.0)\n",
            "Requirement already satisfied: pyyaml>=5.4 in /usr/local/lib/python3.7/dist-packages (from requests-cache~=0.7.0->chembl_webresource_client) (6.0)\n",
            "Requirement already satisfied: url-normalize<2.0,>=1.4 in /usr/local/lib/python3.7/dist-packages (from requests-cache~=0.7.0->chembl_webresource_client) (1.4.3)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from url-normalize<2.0,>=1.4->requests-cache~=0.7.0->chembl_webresource_client) (1.15.0)\n"
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              "                                      cross_references  ... tax_id\n",
              "0                                                   []  ...   9606\n",
              "1                                                   []  ...  12721\n",
              "2    [{'xref_id': 'P15822', 'xref_name': None, 'xre...  ...   9606\n",
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              "..                                                 ...  ...    ...\n",
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              "[265 rows x 9 columns]"
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          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "#import libraries\n",
        "import pandas as pd\n",
        "from chembl_webresource_client.new_client import new_client\n",
        "\n",
        "#searching for the target protein in the chembl database\n",
        "target = new_client.target\n",
        "target_query = target.search('Human immunodeficiency virus')\n",
        "targets = pd.DataFrame.from_dict(target_query)\n",
        "targets"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#selecting the target of choice (HIV integrase)\n",
        "target_df_index = targets.index[targets['pref_name'] == \"Human immunodeficiency virus type 1 integrase\"].tolist()\n",
        "selected_target = targets.target_chembl_id[target_df_index[0]]"
      ],
      "metadata": {
        "id": "YZ3yCwE4HAUN"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#selecting drug activities for HIV integrase target\n",
        "activity = new_client.activity\n",
        "res = activity.filter(target_chembl_id=selected_target).filter(standard_type='IC50')\n",
        "activities = pd.DataFrame.from_dict(res)\n",
        "activities"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "7MCTY4rRHDVF",
        "outputId": "78109acf-782b-462c-96cb-2a235cf85ecf"
      },
      "execution_count": 4,
      "outputs": [
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              "      <th>value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>None</td>\n",
              "      <td>32538</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL701719</td>\n",
              "      <td>Inhibition of HIV-1 integrase, under 1 uM for ...</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000357</td>\n",
              "      <td>single protein format</td>\n",
              "      <td>O=C(/C=C/c1ccc(O)c(O)c1)O[C@H](Cc1ccc(O)c(O)c1...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL1133723</td>\n",
              "      <td>J. Med. Chem.</td>\n",
              "      <td>2000</td>\n",
              "      <td>{'bei': '14.00', 'le': '0.27', 'lle': '3.29', ...</td>\n",
              "      <td>CHEMBL324842</td>\n",
              "      <td>ROSMARINIC ACID</td>\n",
              "      <td>CHEMBL324842</td>\n",
              "      <td>5.05</td>\n",
              "      <td>False</td>\n",
              "      <td>http://www.openphacts.org/units/Nanomolar</td>\n",
              "      <td>115272</td>\n",
              "      <td>=</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>=</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>nM</td>\n",
              "      <td>None</td>\n",
              "      <td>9000.0</td>\n",
              "      <td>CHEMBL3471</td>\n",
              "      <td>Human immunodeficiency virus 1</td>\n",
              "      <td>Human immunodeficiency virus type 1 integrase</td>\n",
              "      <td>11676</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>None</td>\n",
              "      <td>34946</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL701719</td>\n",
              "      <td>Inhibition of HIV-1 integrase, under 1 uM for ...</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000357</td>\n",
              "      <td>single protein format</td>\n",
              "      <td>O=C(CC(O)CCc1ccc(O)c(O)c1)O[C@H]1Cc2cc(O)c(O)c...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL1133723</td>\n",
              "      <td>J. Med. Chem.</td>\n",
              "      <td>2000</td>\n",
              "      <td>{'bei': '11.74', 'le': '0.22', 'lle': '2.96', ...</td>\n",
              "      <td>CHEMBL304722</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL304722</td>\n",
              "      <td>5.85</td>\n",
              "      <td>False</td>\n",
              "      <td>http://www.openphacts.org/units/Nanomolar</td>\n",
              "      <td>115252</td>\n",
              "      <td>=</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>=</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>nM</td>\n",
              "      <td>None</td>\n",
              "      <td>1400.0</td>\n",
              "      <td>CHEMBL3471</td>\n",
              "      <td>Human immunodeficiency virus 1</td>\n",
              "      <td>Human immunodeficiency virus type 1 integrase</td>\n",
              "      <td>11676</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>1.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>None</td>\n",
              "      <td>34947</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL701720</td>\n",
              "      <td>Tested for inhibition of HIV-1 integrase, unde...</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000357</td>\n",
              "      <td>single protein format</td>\n",
              "      <td>O=C(CC(O)CCc1ccc(O)c(O)c1)O[C@H]1Cc2cc(O)c(O)c...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL1133723</td>\n",
              "      <td>J. Med. Chem.</td>\n",
              "      <td>2000</td>\n",
              "      <td>{'bei': '12.04', 'le': '0.23', 'lle': '3.11', ...</td>\n",
              "      <td>CHEMBL304722</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL304722</td>\n",
              "      <td>6.00</td>\n",
              "      <td>False</td>\n",
              "      <td>http://www.openphacts.org/units/Nanomolar</td>\n",
              "      <td>115252</td>\n",
              "      <td>=</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>=</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>nM</td>\n",
              "      <td>None</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>CHEMBL3471</td>\n",
              "      <td>Human immunodeficiency virus 1</td>\n",
              "      <td>Human immunodeficiency virus type 1 integrase</td>\n",
              "      <td>11676</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>None</td>\n",
              "      <td>34948</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL701719</td>\n",
              "      <td>Inhibition of HIV-1 integrase, under 1 uM for ...</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000357</td>\n",
              "      <td>single protein format</td>\n",
              "      <td>O=C(CCc1ccc(O)c(O)c1)c1ccc(O)c(O)c1O</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL1133723</td>\n",
              "      <td>J. Med. Chem.</td>\n",
              "      <td>2000</td>\n",
              "      <td>{'bei': '19.88', 'le': '0.38', 'lle': '3.74', ...</td>\n",
              "      <td>CHEMBL67076</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL67076</td>\n",
              "      <td>5.77</td>\n",
              "      <td>False</td>\n",
              "      <td>http://www.openphacts.org/units/Nanomolar</td>\n",
              "      <td>115251</td>\n",
              "      <td>=</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>=</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>nM</td>\n",
              "      <td>None</td>\n",
              "      <td>1700.0</td>\n",
              "      <td>CHEMBL3471</td>\n",
              "      <td>Human immunodeficiency virus 1</td>\n",
              "      <td>Human immunodeficiency virus type 1 integrase</td>\n",
              "      <td>11676</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>1.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>None</td>\n",
              "      <td>34949</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL701720</td>\n",
              "      <td>Tested for inhibition of HIV-1 integrase, unde...</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000357</td>\n",
              "      <td>single protein format</td>\n",
              "      <td>O=C(CCc1ccc(O)c(O)c1)c1ccc(O)c(O)c1O</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL1133723</td>\n",
              "      <td>J. Med. Chem.</td>\n",
              "      <td>2000</td>\n",
              "      <td>{'bei': '20.67', 'le': '0.39', 'lle': '3.97', ...</td>\n",
              "      <td>CHEMBL67076</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL67076</td>\n",
              "      <td>6.00</td>\n",
              "      <td>False</td>\n",
              "      <td>http://www.openphacts.org/units/Nanomolar</td>\n",
              "      <td>115251</td>\n",
              "      <td>=</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>=</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>nM</td>\n",
              "      <td>None</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>CHEMBL3471</td>\n",
              "      <td>Human immunodeficiency virus 1</td>\n",
              "      <td>Human immunodeficiency virus type 1 integrase</td>\n",
              "      <td>11676</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7560</th>\n",
              "      <td>None</td>\n",
              "      <td>20706754</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL4628341</td>\n",
              "      <td>Inhibition of recombinant HIV1 integrase expre...</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000019</td>\n",
              "      <td>assay format</td>\n",
              "      <td>CN1CCN(c2ccc3nc(-c4ccc5nc(CNC(=O)CCCCN6CCN(CCC...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL4627304</td>\n",
              "      <td>Bioorg Med Chem</td>\n",
              "      <td>2020</td>\n",
              "      <td>{'bei': '5.56', 'le': '0.10', 'lle': '-0.51', ...</td>\n",
              "      <td>CHEMBL4649015</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL4649015</td>\n",
              "      <td>5.41</td>\n",
              "      <td>False</td>\n",
              "      <td>http://www.openphacts.org/units/Nanomolar</td>\n",
              "      <td>3487412</td>\n",
              "      <td>=</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>=</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>nM</td>\n",
              "      <td>None</td>\n",
              "      <td>3900.0</td>\n",
              "      <td>CHEMBL3471</td>\n",
              "      <td>Human immunodeficiency virus 1</td>\n",
              "      <td>Human immunodeficiency virus type 1 integrase</td>\n",
              "      <td>11676</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>3.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7561</th>\n",
              "      <td>None</td>\n",
              "      <td>20706755</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL4628341</td>\n",
              "      <td>Inhibition of recombinant HIV1 integrase expre...</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000019</td>\n",
              "      <td>assay format</td>\n",
              "      <td>CN(C)CCCNC(=O)c1ccc2nc(-c3ccc4nc(CNC(=O)CN5CCN...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL4627304</td>\n",
              "      <td>Bioorg Med Chem</td>\n",
              "      <td>2020</td>\n",
              "      <td>{'bei': '5.98', 'le': '0.11', 'lle': '2.24', '...</td>\n",
              "      <td>CHEMBL4640673</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL4640673</td>\n",
              "      <td>5.68</td>\n",
              "      <td>False</td>\n",
              "      <td>http://www.openphacts.org/units/Nanomolar</td>\n",
              "      <td>3487403</td>\n",
              "      <td>=</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>=</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>nM</td>\n",
              "      <td>None</td>\n",
              "      <td>2100.0</td>\n",
              "      <td>CHEMBL3471</td>\n",
              "      <td>Human immunodeficiency virus 1</td>\n",
              "      <td>Human immunodeficiency virus type 1 integrase</td>\n",
              "      <td>11676</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>2.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7562</th>\n",
              "      <td>None</td>\n",
              "      <td>20706757</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL4628341</td>\n",
              "      <td>Inhibition of recombinant HIV1 integrase expre...</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000019</td>\n",
              "      <td>assay format</td>\n",
              "      <td>CN1CCN(c2ccc3nc(-c4ccc5nc(CNC(=O)CN6CCN(CC(=O)...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL4627304</td>\n",
              "      <td>Bioorg Med Chem</td>\n",
              "      <td>2020</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL4633363</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL4633363</td>\n",
              "      <td>None</td>\n",
              "      <td>False</td>\n",
              "      <td>http://www.openphacts.org/units/Nanomolar</td>\n",
              "      <td>3487409</td>\n",
              "      <td>&gt;</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>&gt;</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>nM</td>\n",
              "      <td>None</td>\n",
              "      <td>15000.0</td>\n",
              "      <td>CHEMBL3471</td>\n",
              "      <td>Human immunodeficiency virus 1</td>\n",
              "      <td>Human immunodeficiency virus type 1 integrase</td>\n",
              "      <td>11676</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7563</th>\n",
              "      <td>None</td>\n",
              "      <td>20706758</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL4628341</td>\n",
              "      <td>Inhibition of recombinant HIV1 integrase expre...</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000019</td>\n",
              "      <td>assay format</td>\n",
              "      <td>CN1CCN(c2ccc3nc(-c4ccc5nc(CNC(=O)CCN6CCN(CCC(=...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL4627304</td>\n",
              "      <td>Bioorg Med Chem</td>\n",
              "      <td>2020</td>\n",
              "      <td>{'bei': '5.62', 'le': '0.10', 'lle': '0.79', '...</td>\n",
              "      <td>CHEMBL4644631</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL4644631</td>\n",
              "      <td>5.16</td>\n",
              "      <td>False</td>\n",
              "      <td>http://www.openphacts.org/units/Nanomolar</td>\n",
              "      <td>3487410</td>\n",
              "      <td>=</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>=</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>nM</td>\n",
              "      <td>None</td>\n",
              "      <td>7000.0</td>\n",
              "      <td>CHEMBL3471</td>\n",
              "      <td>Human immunodeficiency virus 1</td>\n",
              "      <td>Human immunodeficiency virus type 1 integrase</td>\n",
              "      <td>11676</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7564</th>\n",
              "      <td>None</td>\n",
              "      <td>20706759</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL4628341</td>\n",
              "      <td>Inhibition of recombinant HIV1 integrase expre...</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000019</td>\n",
              "      <td>assay format</td>\n",
              "      <td>CN1CCN(c2ccc3nc(-c4ccc5nc(CNC(=O)CCCN6CCN(CCCC...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL4627304</td>\n",
              "      <td>Bioorg Med Chem</td>\n",
              "      <td>2020</td>\n",
              "      <td>{'bei': '5.29', 'le': '0.10', 'lle': '-0.14', ...</td>\n",
              "      <td>CHEMBL4645067</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL4645067</td>\n",
              "      <td>5.00</td>\n",
              "      <td>False</td>\n",
              "      <td>http://www.openphacts.org/units/Nanomolar</td>\n",
              "      <td>3487411</td>\n",
              "      <td>=</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>=</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>nM</td>\n",
              "      <td>None</td>\n",
              "      <td>9900.0</td>\n",
              "      <td>CHEMBL3471</td>\n",
              "      <td>Human immunodeficiency virus 1</td>\n",
              "      <td>Human immunodeficiency virus type 1 integrase</td>\n",
              "      <td>11676</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>9.9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7565 rows × 45 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-85984b54-f4bc-4297-8f88-e9d81bbb81e9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-85984b54-f4bc-4297-8f88-e9d81bbb81e9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "          element.innerHTML = '';\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     activity_comment  activity_id  ... upper_value value\n",
              "0                None        32538  ...        None   9.0\n",
              "1                None        34946  ...        None   1.4\n",
              "2                None        34947  ...        None   1.0\n",
              "3                None        34948  ...        None   1.7\n",
              "4                None        34949  ...        None   1.0\n",
              "...               ...          ...  ...         ...   ...\n",
              "7560             None     20706754  ...        None   3.9\n",
              "7561             None     20706755  ...        None   2.1\n",
              "7562             None     20706757  ...        None  15.0\n",
              "7563             None     20706758  ...        None   7.0\n",
              "7564             None     20706759  ...        None   9.9\n",
              "\n",
              "[7565 rows x 45 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#mounting Google Drive to Google Colab\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive/', force_remount = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hVG7xZU7HJuk",
        "outputId": "f3f4ecfe-26de-41fe-9cfb-2ba38d2ce9ef"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#changing the working directory to a designated folder\n",
        "!mkdir \"/content/gdrive/MyDrive/Colab Notebooks/drug discovery data\"\n",
        "%cd \"/content/gdrive/MyDrive/Colab Notebooks/drug discovery data\""
      ],
      "metadata": {
        "id": "5khF8fzjI-r8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a702f5c2-7d17-4868-94ab-e24196daf983"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mkdir: cannot create directory ‘/content/gdrive/MyDrive/Colab Notebooks/drug discovery data’: File exists\n",
            "/content/gdrive/MyDrive/Colab Notebooks/drug discovery data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#save the activities to a csv file\n",
        "activities.to_csv('bioactivity_data.csv', index=False)"
      ],
      "metadata": {
        "id": "NMrjKfLzHIBO"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#removing entries without standard_value or canonical_smiles\n",
        "activities = activities[activities.standard_value.notna()]\n",
        "activities = activities[activities.canonical_smiles.notna()]\n",
        "activities"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "MnOa2kXWI_PZ",
        "outputId": "09dae63f-1ff4-4f29-fb5e-d66bf050f65e"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-30b403c4-7d07-420f-9e04-1dedc0fbe25e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>activity_comment</th>\n",
              "      <th>activity_id</th>\n",
              "      <th>activity_properties</th>\n",
              "      <th>assay_chembl_id</th>\n",
              "      <th>assay_description</th>\n",
              "      <th>assay_type</th>\n",
              "      <th>assay_variant_accession</th>\n",
              "      <th>assay_variant_mutation</th>\n",
              "      <th>bao_endpoint</th>\n",
              "      <th>bao_format</th>\n",
              "      <th>bao_label</th>\n",
              "      <th>canonical_smiles</th>\n",
              "      <th>data_validity_comment</th>\n",
              "      <th>data_validity_description</th>\n",
              "      <th>document_chembl_id</th>\n",
              "      <th>document_journal</th>\n",
              "      <th>document_year</th>\n",
              "      <th>ligand_efficiency</th>\n",
              "      <th>molecule_chembl_id</th>\n",
              "      <th>molecule_pref_name</th>\n",
              "      <th>parent_molecule_chembl_id</th>\n",
              "      <th>pchembl_value</th>\n",
              "      <th>potential_duplicate</th>\n",
              "      <th>qudt_units</th>\n",
              "      <th>record_id</th>\n",
              "      <th>relation</th>\n",
              "      <th>src_id</th>\n",
              "      <th>standard_flag</th>\n",
              "      <th>standard_relation</th>\n",
              "      <th>standard_text_value</th>\n",
              "      <th>standard_type</th>\n",
              "      <th>standard_units</th>\n",
              "      <th>standard_upper_value</th>\n",
              "      <th>standard_value</th>\n",
              "      <th>target_chembl_id</th>\n",
              "      <th>target_organism</th>\n",
              "      <th>target_pref_name</th>\n",
              "      <th>target_tax_id</th>\n",
              "      <th>text_value</th>\n",
              "      <th>toid</th>\n",
              "      <th>type</th>\n",
              "      <th>units</th>\n",
              "      <th>uo_units</th>\n",
              "      <th>upper_value</th>\n",
              "      <th>value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>None</td>\n",
              "      <td>32538</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL701719</td>\n",
              "      <td>Inhibition of HIV-1 integrase, under 1 uM for ...</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000357</td>\n",
              "      <td>single protein format</td>\n",
              "      <td>O=C(/C=C/c1ccc(O)c(O)c1)O[C@H](Cc1ccc(O)c(O)c1...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL1133723</td>\n",
              "      <td>J. Med. Chem.</td>\n",
              "      <td>2000</td>\n",
              "      <td>{'bei': '14.00', 'le': '0.27', 'lle': '3.29', ...</td>\n",
              "      <td>CHEMBL324842</td>\n",
              "      <td>ROSMARINIC ACID</td>\n",
              "      <td>CHEMBL324842</td>\n",
              "      <td>5.05</td>\n",
              "      <td>False</td>\n",
              "      <td>http://www.openphacts.org/units/Nanomolar</td>\n",
              "      <td>115272</td>\n",
              "      <td>=</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>=</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>nM</td>\n",
              "      <td>None</td>\n",
              "      <td>9000.0</td>\n",
              "      <td>CHEMBL3471</td>\n",
              "      <td>Human immunodeficiency virus 1</td>\n",
              "      <td>Human immunodeficiency virus type 1 integrase</td>\n",
              "      <td>11676</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>None</td>\n",
              "      <td>34946</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL701719</td>\n",
              "      <td>Inhibition of HIV-1 integrase, under 1 uM for ...</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000357</td>\n",
              "      <td>single protein format</td>\n",
              "      <td>O=C(CC(O)CCc1ccc(O)c(O)c1)O[C@H]1Cc2cc(O)c(O)c...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL1133723</td>\n",
              "      <td>J. Med. Chem.</td>\n",
              "      <td>2000</td>\n",
              "      <td>{'bei': '11.74', 'le': '0.22', 'lle': '2.96', ...</td>\n",
              "      <td>CHEMBL304722</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL304722</td>\n",
              "      <td>5.85</td>\n",
              "      <td>False</td>\n",
              "      <td>http://www.openphacts.org/units/Nanomolar</td>\n",
              "      <td>115252</td>\n",
              "      <td>=</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>=</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>nM</td>\n",
              "      <td>None</td>\n",
              "      <td>1400.0</td>\n",
              "      <td>CHEMBL3471</td>\n",
              "      <td>Human immunodeficiency virus 1</td>\n",
              "      <td>Human immunodeficiency virus type 1 integrase</td>\n",
              "      <td>11676</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>1.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>None</td>\n",
              "      <td>34947</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL701720</td>\n",
              "      <td>Tested for inhibition of HIV-1 integrase, unde...</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000357</td>\n",
              "      <td>single protein format</td>\n",
              "      <td>O=C(CC(O)CCc1ccc(O)c(O)c1)O[C@H]1Cc2cc(O)c(O)c...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL1133723</td>\n",
              "      <td>J. Med. Chem.</td>\n",
              "      <td>2000</td>\n",
              "      <td>{'bei': '12.04', 'le': '0.23', 'lle': '3.11', ...</td>\n",
              "      <td>CHEMBL304722</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL304722</td>\n",
              "      <td>6.00</td>\n",
              "      <td>False</td>\n",
              "      <td>http://www.openphacts.org/units/Nanomolar</td>\n",
              "      <td>115252</td>\n",
              "      <td>=</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>=</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>nM</td>\n",
              "      <td>None</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>CHEMBL3471</td>\n",
              "      <td>Human immunodeficiency virus 1</td>\n",
              "      <td>Human immunodeficiency virus type 1 integrase</td>\n",
              "      <td>11676</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>None</td>\n",
              "      <td>34948</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL701719</td>\n",
              "      <td>Inhibition of HIV-1 integrase, under 1 uM for ...</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000357</td>\n",
              "      <td>single protein format</td>\n",
              "      <td>O=C(CCc1ccc(O)c(O)c1)c1ccc(O)c(O)c1O</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL1133723</td>\n",
              "      <td>J. Med. Chem.</td>\n",
              "      <td>2000</td>\n",
              "      <td>{'bei': '19.88', 'le': '0.38', 'lle': '3.74', ...</td>\n",
              "      <td>CHEMBL67076</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL67076</td>\n",
              "      <td>5.77</td>\n",
              "      <td>False</td>\n",
              "      <td>http://www.openphacts.org/units/Nanomolar</td>\n",
              "      <td>115251</td>\n",
              "      <td>=</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>=</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>nM</td>\n",
              "      <td>None</td>\n",
              "      <td>1700.0</td>\n",
              "      <td>CHEMBL3471</td>\n",
              "      <td>Human immunodeficiency virus 1</td>\n",
              "      <td>Human immunodeficiency virus type 1 integrase</td>\n",
              "      <td>11676</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>1.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>None</td>\n",
              "      <td>34949</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL701720</td>\n",
              "      <td>Tested for inhibition of HIV-1 integrase, unde...</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000357</td>\n",
              "      <td>single protein format</td>\n",
              "      <td>O=C(CCc1ccc(O)c(O)c1)c1ccc(O)c(O)c1O</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL1133723</td>\n",
              "      <td>J. Med. Chem.</td>\n",
              "      <td>2000</td>\n",
              "      <td>{'bei': '20.67', 'le': '0.39', 'lle': '3.97', ...</td>\n",
              "      <td>CHEMBL67076</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL67076</td>\n",
              "      <td>6.00</td>\n",
              "      <td>False</td>\n",
              "      <td>http://www.openphacts.org/units/Nanomolar</td>\n",
              "      <td>115251</td>\n",
              "      <td>=</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>=</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>nM</td>\n",
              "      <td>None</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>CHEMBL3471</td>\n",
              "      <td>Human immunodeficiency virus 1</td>\n",
              "      <td>Human immunodeficiency virus type 1 integrase</td>\n",
              "      <td>11676</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7560</th>\n",
              "      <td>None</td>\n",
              "      <td>20706754</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL4628341</td>\n",
              "      <td>Inhibition of recombinant HIV1 integrase expre...</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000019</td>\n",
              "      <td>assay format</td>\n",
              "      <td>CN1CCN(c2ccc3nc(-c4ccc5nc(CNC(=O)CCCCN6CCN(CCC...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL4627304</td>\n",
              "      <td>Bioorg Med Chem</td>\n",
              "      <td>2020</td>\n",
              "      <td>{'bei': '5.56', 'le': '0.10', 'lle': '-0.51', ...</td>\n",
              "      <td>CHEMBL4649015</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL4649015</td>\n",
              "      <td>5.41</td>\n",
              "      <td>False</td>\n",
              "      <td>http://www.openphacts.org/units/Nanomolar</td>\n",
              "      <td>3487412</td>\n",
              "      <td>=</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>=</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>nM</td>\n",
              "      <td>None</td>\n",
              "      <td>3900.0</td>\n",
              "      <td>CHEMBL3471</td>\n",
              "      <td>Human immunodeficiency virus 1</td>\n",
              "      <td>Human immunodeficiency virus type 1 integrase</td>\n",
              "      <td>11676</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>3.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7561</th>\n",
              "      <td>None</td>\n",
              "      <td>20706755</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL4628341</td>\n",
              "      <td>Inhibition of recombinant HIV1 integrase expre...</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000019</td>\n",
              "      <td>assay format</td>\n",
              "      <td>CN(C)CCCNC(=O)c1ccc2nc(-c3ccc4nc(CNC(=O)CN5CCN...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL4627304</td>\n",
              "      <td>Bioorg Med Chem</td>\n",
              "      <td>2020</td>\n",
              "      <td>{'bei': '5.98', 'le': '0.11', 'lle': '2.24', '...</td>\n",
              "      <td>CHEMBL4640673</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL4640673</td>\n",
              "      <td>5.68</td>\n",
              "      <td>False</td>\n",
              "      <td>http://www.openphacts.org/units/Nanomolar</td>\n",
              "      <td>3487403</td>\n",
              "      <td>=</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>=</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>nM</td>\n",
              "      <td>None</td>\n",
              "      <td>2100.0</td>\n",
              "      <td>CHEMBL3471</td>\n",
              "      <td>Human immunodeficiency virus 1</td>\n",
              "      <td>Human immunodeficiency virus type 1 integrase</td>\n",
              "      <td>11676</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>2.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7562</th>\n",
              "      <td>None</td>\n",
              "      <td>20706757</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL4628341</td>\n",
              "      <td>Inhibition of recombinant HIV1 integrase expre...</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000019</td>\n",
              "      <td>assay format</td>\n",
              "      <td>CN1CCN(c2ccc3nc(-c4ccc5nc(CNC(=O)CN6CCN(CC(=O)...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL4627304</td>\n",
              "      <td>Bioorg Med Chem</td>\n",
              "      <td>2020</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL4633363</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL4633363</td>\n",
              "      <td>None</td>\n",
              "      <td>False</td>\n",
              "      <td>http://www.openphacts.org/units/Nanomolar</td>\n",
              "      <td>3487409</td>\n",
              "      <td>&gt;</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>&gt;</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>nM</td>\n",
              "      <td>None</td>\n",
              "      <td>15000.0</td>\n",
              "      <td>CHEMBL3471</td>\n",
              "      <td>Human immunodeficiency virus 1</td>\n",
              "      <td>Human immunodeficiency virus type 1 integrase</td>\n",
              "      <td>11676</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7563</th>\n",
              "      <td>None</td>\n",
              "      <td>20706758</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL4628341</td>\n",
              "      <td>Inhibition of recombinant HIV1 integrase expre...</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000019</td>\n",
              "      <td>assay format</td>\n",
              "      <td>CN1CCN(c2ccc3nc(-c4ccc5nc(CNC(=O)CCN6CCN(CCC(=...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL4627304</td>\n",
              "      <td>Bioorg Med Chem</td>\n",
              "      <td>2020</td>\n",
              "      <td>{'bei': '5.62', 'le': '0.10', 'lle': '0.79', '...</td>\n",
              "      <td>CHEMBL4644631</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL4644631</td>\n",
              "      <td>5.16</td>\n",
              "      <td>False</td>\n",
              "      <td>http://www.openphacts.org/units/Nanomolar</td>\n",
              "      <td>3487410</td>\n",
              "      <td>=</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>=</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>nM</td>\n",
              "      <td>None</td>\n",
              "      <td>7000.0</td>\n",
              "      <td>CHEMBL3471</td>\n",
              "      <td>Human immunodeficiency virus 1</td>\n",
              "      <td>Human immunodeficiency virus type 1 integrase</td>\n",
              "      <td>11676</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7564</th>\n",
              "      <td>None</td>\n",
              "      <td>20706759</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL4628341</td>\n",
              "      <td>Inhibition of recombinant HIV1 integrase expre...</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000019</td>\n",
              "      <td>assay format</td>\n",
              "      <td>CN1CCN(c2ccc3nc(-c4ccc5nc(CNC(=O)CCCN6CCN(CCCC...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL4627304</td>\n",
              "      <td>Bioorg Med Chem</td>\n",
              "      <td>2020</td>\n",
              "      <td>{'bei': '5.29', 'le': '0.10', 'lle': '-0.14', ...</td>\n",
              "      <td>CHEMBL4645067</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL4645067</td>\n",
              "      <td>5.00</td>\n",
              "      <td>False</td>\n",
              "      <td>http://www.openphacts.org/units/Nanomolar</td>\n",
              "      <td>3487411</td>\n",
              "      <td>=</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>=</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>nM</td>\n",
              "      <td>None</td>\n",
              "      <td>9900.0</td>\n",
              "      <td>CHEMBL3471</td>\n",
              "      <td>Human immunodeficiency virus 1</td>\n",
              "      <td>Human immunodeficiency virus type 1 integrase</td>\n",
              "      <td>11676</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>9.9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7444 rows × 45 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-30b403c4-7d07-420f-9e04-1dedc0fbe25e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      fill: #1967D2;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-30b403c4-7d07-420f-9e04-1dedc0fbe25e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-30b403c4-7d07-420f-9e04-1dedc0fbe25e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     activity_comment  activity_id  ... upper_value value\n",
              "0                None        32538  ...        None   9.0\n",
              "1                None        34946  ...        None   1.4\n",
              "2                None        34947  ...        None   1.0\n",
              "3                None        34948  ...        None   1.7\n",
              "4                None        34949  ...        None   1.0\n",
              "...               ...          ...  ...         ...   ...\n",
              "7560             None     20706754  ...        None   3.9\n",
              "7561             None     20706755  ...        None   2.1\n",
              "7562             None     20706757  ...        None  15.0\n",
              "7563             None     20706758  ...        None   7.0\n",
              "7564             None     20706759  ...        None   9.9\n",
              "\n",
              "[7444 rows x 45 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#checking number of unique compounds\n",
        "len(activities.canonical_smiles.unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LWejVK-5I_n9",
        "outputId": "434bebb9-91b9-4285-a47e-8eec8fae4708"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4072"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#removing non-unique compounds\n",
        "activities_nr = activities.drop_duplicates(['canonical_smiles'])\n",
        "activities_nr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "P0SQKJwURTlm",
        "outputId": "724a9de9-c5a2-48d9-87d0-14816d02c4a7"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-9c7f6f5d-abc4-431d-a152-fcc0ed56806a\">\n",
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              "        vertical-align: middle;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>activity_comment</th>\n",
              "      <th>activity_id</th>\n",
              "      <th>activity_properties</th>\n",
              "      <th>assay_chembl_id</th>\n",
              "      <th>assay_description</th>\n",
              "      <th>assay_type</th>\n",
              "      <th>assay_variant_accession</th>\n",
              "      <th>assay_variant_mutation</th>\n",
              "      <th>bao_endpoint</th>\n",
              "      <th>bao_format</th>\n",
              "      <th>bao_label</th>\n",
              "      <th>canonical_smiles</th>\n",
              "      <th>data_validity_comment</th>\n",
              "      <th>data_validity_description</th>\n",
              "      <th>document_chembl_id</th>\n",
              "      <th>document_journal</th>\n",
              "      <th>document_year</th>\n",
              "      <th>ligand_efficiency</th>\n",
              "      <th>molecule_chembl_id</th>\n",
              "      <th>molecule_pref_name</th>\n",
              "      <th>parent_molecule_chembl_id</th>\n",
              "      <th>pchembl_value</th>\n",
              "      <th>potential_duplicate</th>\n",
              "      <th>qudt_units</th>\n",
              "      <th>record_id</th>\n",
              "      <th>relation</th>\n",
              "      <th>src_id</th>\n",
              "      <th>standard_flag</th>\n",
              "      <th>standard_relation</th>\n",
              "      <th>standard_text_value</th>\n",
              "      <th>standard_type</th>\n",
              "      <th>standard_units</th>\n",
              "      <th>standard_upper_value</th>\n",
              "      <th>standard_value</th>\n",
              "      <th>target_chembl_id</th>\n",
              "      <th>target_organism</th>\n",
              "      <th>target_pref_name</th>\n",
              "      <th>target_tax_id</th>\n",
              "      <th>text_value</th>\n",
              "      <th>toid</th>\n",
              "      <th>type</th>\n",
              "      <th>units</th>\n",
              "      <th>uo_units</th>\n",
              "      <th>upper_value</th>\n",
              "      <th>value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>None</td>\n",
              "      <td>32538</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL701719</td>\n",
              "      <td>Inhibition of HIV-1 integrase, under 1 uM for ...</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000357</td>\n",
              "      <td>single protein format</td>\n",
              "      <td>O=C(/C=C/c1ccc(O)c(O)c1)O[C@H](Cc1ccc(O)c(O)c1...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL1133723</td>\n",
              "      <td>J. Med. Chem.</td>\n",
              "      <td>2000</td>\n",
              "      <td>{'bei': '14.00', 'le': '0.27', 'lle': '3.29', ...</td>\n",
              "      <td>CHEMBL324842</td>\n",
              "      <td>ROSMARINIC ACID</td>\n",
              "      <td>CHEMBL324842</td>\n",
              "      <td>5.05</td>\n",
              "      <td>False</td>\n",
              "      <td>http://www.openphacts.org/units/Nanomolar</td>\n",
              "      <td>115272</td>\n",
              "      <td>=</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>=</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>nM</td>\n",
              "      <td>None</td>\n",
              "      <td>9000.0</td>\n",
              "      <td>CHEMBL3471</td>\n",
              "      <td>Human immunodeficiency virus 1</td>\n",
              "      <td>Human immunodeficiency virus type 1 integrase</td>\n",
              "      <td>11676</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>None</td>\n",
              "      <td>34946</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL701719</td>\n",
              "      <td>Inhibition of HIV-1 integrase, under 1 uM for ...</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000357</td>\n",
              "      <td>single protein format</td>\n",
              "      <td>O=C(CC(O)CCc1ccc(O)c(O)c1)O[C@H]1Cc2cc(O)c(O)c...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL1133723</td>\n",
              "      <td>J. Med. Chem.</td>\n",
              "      <td>2000</td>\n",
              "      <td>{'bei': '11.74', 'le': '0.22', 'lle': '2.96', ...</td>\n",
              "      <td>CHEMBL304722</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL304722</td>\n",
              "      <td>5.85</td>\n",
              "      <td>False</td>\n",
              "      <td>http://www.openphacts.org/units/Nanomolar</td>\n",
              "      <td>115252</td>\n",
              "      <td>=</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>=</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>nM</td>\n",
              "      <td>None</td>\n",
              "      <td>1400.0</td>\n",
              "      <td>CHEMBL3471</td>\n",
              "      <td>Human immunodeficiency virus 1</td>\n",
              "      <td>Human immunodeficiency virus type 1 integrase</td>\n",
              "      <td>11676</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>1.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>None</td>\n",
              "      <td>34948</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL701719</td>\n",
              "      <td>Inhibition of HIV-1 integrase, under 1 uM for ...</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000357</td>\n",
              "      <td>single protein format</td>\n",
              "      <td>O=C(CCc1ccc(O)c(O)c1)c1ccc(O)c(O)c1O</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL1133723</td>\n",
              "      <td>J. Med. Chem.</td>\n",
              "      <td>2000</td>\n",
              "      <td>{'bei': '19.88', 'le': '0.38', 'lle': '3.74', ...</td>\n",
              "      <td>CHEMBL67076</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL67076</td>\n",
              "      <td>5.77</td>\n",
              "      <td>False</td>\n",
              "      <td>http://www.openphacts.org/units/Nanomolar</td>\n",
              "      <td>115251</td>\n",
              "      <td>=</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>=</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>nM</td>\n",
              "      <td>None</td>\n",
              "      <td>1700.0</td>\n",
              "      <td>CHEMBL3471</td>\n",
              "      <td>Human immunodeficiency virus 1</td>\n",
              "      <td>Human immunodeficiency virus type 1 integrase</td>\n",
              "      <td>11676</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>1.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>None</td>\n",
              "      <td>34953</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL701719</td>\n",
              "      <td>Inhibition of HIV-1 integrase, under 1 uM for ...</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000357</td>\n",
              "      <td>single protein format</td>\n",
              "      <td>O=C(/C=C/c1ccc(O)c(O)c1)O[C@H]1[C@H](O)C[C@](O...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL1133723</td>\n",
              "      <td>J. Med. Chem.</td>\n",
              "      <td>2000</td>\n",
              "      <td>{'bei': '12.78', 'le': '0.24', 'lle': '5.57', ...</td>\n",
              "      <td>CHEMBL177126</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL177126</td>\n",
              "      <td>6.60</td>\n",
              "      <td>False</td>\n",
              "      <td>http://www.openphacts.org/units/Nanomolar</td>\n",
              "      <td>115281</td>\n",
              "      <td>=</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>=</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>nM</td>\n",
              "      <td>None</td>\n",
              "      <td>250.0</td>\n",
              "      <td>CHEMBL3471</td>\n",
              "      <td>Human immunodeficiency virus 1</td>\n",
              "      <td>Human immunodeficiency virus type 1 integrase</td>\n",
              "      <td>11676</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>0.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>None</td>\n",
              "      <td>35003</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL701899</td>\n",
              "      <td>Inhibition of recombinant HIV-1 Integrase in s...</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000357</td>\n",
              "      <td>single protein format</td>\n",
              "      <td>O=C(O)C(=O)CC(=O)c1cccc(Cc2cccc(Cl)c2)c1</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL1133559</td>\n",
              "      <td>J. Med. Chem.</td>\n",
              "      <td>2000</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL145335</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL145335</td>\n",
              "      <td>None</td>\n",
              "      <td>False</td>\n",
              "      <td>http://www.openphacts.org/units/Nanomolar</td>\n",
              "      <td>283574</td>\n",
              "      <td>&lt;</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>&lt;</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>nM</td>\n",
              "      <td>None</td>\n",
              "      <td>100.0</td>\n",
              "      <td>CHEMBL3471</td>\n",
              "      <td>Human immunodeficiency virus 1</td>\n",
              "      <td>Human immunodeficiency virus type 1 integrase</td>\n",
              "      <td>11676</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7560</th>\n",
              "      <td>None</td>\n",
              "      <td>20706754</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL4628341</td>\n",
              "      <td>Inhibition of recombinant HIV1 integrase expre...</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000019</td>\n",
              "      <td>assay format</td>\n",
              "      <td>CN1CCN(c2ccc3nc(-c4ccc5nc(CNC(=O)CCCCN6CCN(CCC...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL4627304</td>\n",
              "      <td>Bioorg Med Chem</td>\n",
              "      <td>2020</td>\n",
              "      <td>{'bei': '5.56', 'le': '0.10', 'lle': '-0.51', ...</td>\n",
              "      <td>CHEMBL4649015</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL4649015</td>\n",
              "      <td>5.41</td>\n",
              "      <td>False</td>\n",
              "      <td>http://www.openphacts.org/units/Nanomolar</td>\n",
              "      <td>3487412</td>\n",
              "      <td>=</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>=</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>nM</td>\n",
              "      <td>None</td>\n",
              "      <td>3900.0</td>\n",
              "      <td>CHEMBL3471</td>\n",
              "      <td>Human immunodeficiency virus 1</td>\n",
              "      <td>Human immunodeficiency virus type 1 integrase</td>\n",
              "      <td>11676</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>3.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7561</th>\n",
              "      <td>None</td>\n",
              "      <td>20706755</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL4628341</td>\n",
              "      <td>Inhibition of recombinant HIV1 integrase expre...</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000019</td>\n",
              "      <td>assay format</td>\n",
              "      <td>CN(C)CCCNC(=O)c1ccc2nc(-c3ccc4nc(CNC(=O)CN5CCN...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL4627304</td>\n",
              "      <td>Bioorg Med Chem</td>\n",
              "      <td>2020</td>\n",
              "      <td>{'bei': '5.98', 'le': '0.11', 'lle': '2.24', '...</td>\n",
              "      <td>CHEMBL4640673</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL4640673</td>\n",
              "      <td>5.68</td>\n",
              "      <td>False</td>\n",
              "      <td>http://www.openphacts.org/units/Nanomolar</td>\n",
              "      <td>3487403</td>\n",
              "      <td>=</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>=</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>nM</td>\n",
              "      <td>None</td>\n",
              "      <td>2100.0</td>\n",
              "      <td>CHEMBL3471</td>\n",
              "      <td>Human immunodeficiency virus 1</td>\n",
              "      <td>Human immunodeficiency virus type 1 integrase</td>\n",
              "      <td>11676</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>2.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7562</th>\n",
              "      <td>None</td>\n",
              "      <td>20706757</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL4628341</td>\n",
              "      <td>Inhibition of recombinant HIV1 integrase expre...</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000019</td>\n",
              "      <td>assay format</td>\n",
              "      <td>CN1CCN(c2ccc3nc(-c4ccc5nc(CNC(=O)CN6CCN(CC(=O)...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL4627304</td>\n",
              "      <td>Bioorg Med Chem</td>\n",
              "      <td>2020</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL4633363</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL4633363</td>\n",
              "      <td>None</td>\n",
              "      <td>False</td>\n",
              "      <td>http://www.openphacts.org/units/Nanomolar</td>\n",
              "      <td>3487409</td>\n",
              "      <td>&gt;</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>&gt;</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>nM</td>\n",
              "      <td>None</td>\n",
              "      <td>15000.0</td>\n",
              "      <td>CHEMBL3471</td>\n",
              "      <td>Human immunodeficiency virus 1</td>\n",
              "      <td>Human immunodeficiency virus type 1 integrase</td>\n",
              "      <td>11676</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7563</th>\n",
              "      <td>None</td>\n",
              "      <td>20706758</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL4628341</td>\n",
              "      <td>Inhibition of recombinant HIV1 integrase expre...</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000019</td>\n",
              "      <td>assay format</td>\n",
              "      <td>CN1CCN(c2ccc3nc(-c4ccc5nc(CNC(=O)CCN6CCN(CCC(=...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL4627304</td>\n",
              "      <td>Bioorg Med Chem</td>\n",
              "      <td>2020</td>\n",
              "      <td>{'bei': '5.62', 'le': '0.10', 'lle': '0.79', '...</td>\n",
              "      <td>CHEMBL4644631</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL4644631</td>\n",
              "      <td>5.16</td>\n",
              "      <td>False</td>\n",
              "      <td>http://www.openphacts.org/units/Nanomolar</td>\n",
              "      <td>3487410</td>\n",
              "      <td>=</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>=</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>nM</td>\n",
              "      <td>None</td>\n",
              "      <td>7000.0</td>\n",
              "      <td>CHEMBL3471</td>\n",
              "      <td>Human immunodeficiency virus 1</td>\n",
              "      <td>Human immunodeficiency virus type 1 integrase</td>\n",
              "      <td>11676</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7564</th>\n",
              "      <td>None</td>\n",
              "      <td>20706759</td>\n",
              "      <td>[]</td>\n",
              "      <td>CHEMBL4628341</td>\n",
              "      <td>Inhibition of recombinant HIV1 integrase expre...</td>\n",
              "      <td>B</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>BAO_0000190</td>\n",
              "      <td>BAO_0000019</td>\n",
              "      <td>assay format</td>\n",
              "      <td>CN1CCN(c2ccc3nc(-c4ccc5nc(CNC(=O)CCCN6CCN(CCCC...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL4627304</td>\n",
              "      <td>Bioorg Med Chem</td>\n",
              "      <td>2020</td>\n",
              "      <td>{'bei': '5.29', 'le': '0.10', 'lle': '-0.14', ...</td>\n",
              "      <td>CHEMBL4645067</td>\n",
              "      <td>None</td>\n",
              "      <td>CHEMBL4645067</td>\n",
              "      <td>5.00</td>\n",
              "      <td>False</td>\n",
              "      <td>http://www.openphacts.org/units/Nanomolar</td>\n",
              "      <td>3487411</td>\n",
              "      <td>=</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>=</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>nM</td>\n",
              "      <td>None</td>\n",
              "      <td>9900.0</td>\n",
              "      <td>CHEMBL3471</td>\n",
              "      <td>Human immunodeficiency virus 1</td>\n",
              "      <td>Human immunodeficiency virus type 1 integrase</td>\n",
              "      <td>11676</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>UO_0000065</td>\n",
              "      <td>None</td>\n",
              "      <td>9.9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4072 rows × 45 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9c7f6f5d-abc4-431d-a152-fcc0ed56806a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9c7f6f5d-abc4-431d-a152-fcc0ed56806a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9c7f6f5d-abc4-431d-a152-fcc0ed56806a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     activity_comment  activity_id  ... upper_value value\n",
              "0                None        32538  ...        None   9.0\n",
              "1                None        34946  ...        None   1.4\n",
              "3                None        34948  ...        None   1.7\n",
              "5                None        34953  ...        None  0.25\n",
              "7                None        35003  ...        None   0.1\n",
              "...               ...          ...  ...         ...   ...\n",
              "7560             None     20706754  ...        None   3.9\n",
              "7561             None     20706755  ...        None   2.1\n",
              "7562             None     20706757  ...        None  15.0\n",
              "7563             None     20706758  ...        None   7.0\n",
              "7564             None     20706759  ...        None   9.9\n",
              "\n",
              "[4072 rows x 45 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#trimming the dataframe down to useful columns and saving preprocessed data to csv\n",
        "activities_nr_trimmed = activities_nr[[\"molecule_chembl_id\", \"canonical_smiles\", \"standard_value\"]]\n",
        "activities_nr_trimmed.to_csv(\"HIV1_integrase_inhibitor_bioactivity_data_preprocessed.csv\", index = False)"
      ],
      "metadata": {
        "id": "l8ghcY043M0q"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#classifying the compounds into bioactivity classes: active (IC50<=1000nM), intermediate (IC50>=10000nM), inactive (1000nM<IC50<10000nM)\n",
        "bioactivity_class = []\n",
        "for IC50 in activities_nr_trimmed.standard_value:\n",
        "  if float(IC50) >= 10000:\n",
        "    bioactivity_class.append(\"inactive\")\n",
        "  elif float(IC50) <= 1000:\n",
        "    bioactivity_class.append(\"active\")\n",
        "  else:\n",
        "    bioactivity_class.append(\"intermediate\")  "
      ],
      "metadata": {
        "id": "-snojtu4RgfM"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#combining the dataframe and classifications\n",
        "activities_nr_trimmed = activities_nr_trimmed.reset_index(drop=True)\n",
        "activities_final = pd.concat([activities_nr_trimmed, pd.Series(bioactivity_class)], axis = 1)\n",
        "activities_final"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "R9xs8Xa7i9zF",
        "outputId": "9c178d55-37a1-4cdc-cb8c-a7bf36ec88b3"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-0747310b-f9a8-4a41-ae2e-2e4c310ccbc4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>molecule_chembl_id</th>\n",
              "      <th>canonical_smiles</th>\n",
              "      <th>standard_value</th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CHEMBL324842</td>\n",
              "      <td>O=C(/C=C/c1ccc(O)c(O)c1)O[C@H](Cc1ccc(O)c(O)c1...</td>\n",
              "      <td>9000.0</td>\n",
              "      <td>intermediate</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CHEMBL304722</td>\n",
              "      <td>O=C(CC(O)CCc1ccc(O)c(O)c1)O[C@H]1Cc2cc(O)c(O)c...</td>\n",
              "      <td>1400.0</td>\n",
              "      <td>intermediate</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CHEMBL67076</td>\n",
              "      <td>O=C(CCc1ccc(O)c(O)c1)c1ccc(O)c(O)c1O</td>\n",
              "      <td>1700.0</td>\n",
              "      <td>intermediate</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CHEMBL177126</td>\n",
              "      <td>O=C(/C=C/c1ccc(O)c(O)c1)O[C@H]1[C@H](O)C[C@](O...</td>\n",
              "      <td>250.0</td>\n",
              "      <td>active</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CHEMBL145335</td>\n",
              "      <td>O=C(O)C(=O)CC(=O)c1cccc(Cc2cccc(Cl)c2)c1</td>\n",
              "      <td>100.0</td>\n",
              "      <td>active</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4067</th>\n",
              "      <td>CHEMBL4649015</td>\n",
              "      <td>CN1CCN(c2ccc3nc(-c4ccc5nc(CNC(=O)CCCCN6CCN(CCC...</td>\n",
              "      <td>3900.0</td>\n",
              "      <td>intermediate</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4068</th>\n",
              "      <td>CHEMBL4640673</td>\n",
              "      <td>CN(C)CCCNC(=O)c1ccc2nc(-c3ccc4nc(CNC(=O)CN5CCN...</td>\n",
              "      <td>2100.0</td>\n",
              "      <td>intermediate</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4069</th>\n",
              "      <td>CHEMBL4633363</td>\n",
              "      <td>CN1CCN(c2ccc3nc(-c4ccc5nc(CNC(=O)CN6CCN(CC(=O)...</td>\n",
              "      <td>15000.0</td>\n",
              "      <td>inactive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4070</th>\n",
              "      <td>CHEMBL4644631</td>\n",
              "      <td>CN1CCN(c2ccc3nc(-c4ccc5nc(CNC(=O)CCN6CCN(CCC(=...</td>\n",
              "      <td>7000.0</td>\n",
              "      <td>intermediate</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4071</th>\n",
              "      <td>CHEMBL4645067</td>\n",
              "      <td>CN1CCN(c2ccc3nc(-c4ccc5nc(CNC(=O)CCCN6CCN(CCCC...</td>\n",
              "      <td>9900.0</td>\n",
              "      <td>intermediate</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4072 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0747310b-f9a8-4a41-ae2e-2e4c310ccbc4')\"\n",
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              "        \n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0747310b-f9a8-4a41-ae2e-2e4c310ccbc4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0747310b-f9a8-4a41-ae2e-2e4c310ccbc4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     molecule_chembl_id  ...             0\n",
              "0          CHEMBL324842  ...  intermediate\n",
              "1          CHEMBL304722  ...  intermediate\n",
              "2           CHEMBL67076  ...  intermediate\n",
              "3          CHEMBL177126  ...        active\n",
              "4          CHEMBL145335  ...        active\n",
              "...                 ...  ...           ...\n",
              "4067      CHEMBL4649015  ...  intermediate\n",
              "4068      CHEMBL4640673  ...  intermediate\n",
              "4069      CHEMBL4633363  ...      inactive\n",
              "4070      CHEMBL4644631  ...  intermediate\n",
              "4071      CHEMBL4645067  ...  intermediate\n",
              "\n",
              "[4072 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#saving the curated dataframe to csv\n",
        "activities_final.to_csv(\"HIV1_integrase_inhibitor_bioactivity_data_curated.csv\", index=False)"
      ],
      "metadata": {
        "id": "UlKsoglNi9sD"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#compressing all data files to a single zip\n",
        "! zip integrase.zip *.csv"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6sZELNhPi9cj",
        "outputId": "7346aecd-a9bc-472a-f174-ce325bea3c61"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "updating: bioactivity_data.csv (deflated 93%)\n",
            "updating: HIV1_integrase_inhibitor_bioactivity_data_curated.csv (deflated 84%)\n",
            "updating: HIV1_integrase_inhibitor_bioactivity_data_preprocessed.csv (deflated 83%)\n",
            "updating: HIV1_integrase_inhibitor_2class_pIC50.csv (deflated 79%)\n",
            "updating: HIV1_integrase_inhibitor_3class_pIC50.csv (deflated 79%)\n",
            "updating: mannwhitneyu_LogP.csv (deflated 8%)\n",
            "updating: mannwhitneyu_MW.csv (deflated 8%)\n",
            "updating: mannwhitneyu_NumHAcceptors.csv (deflated 11%)\n",
            "updating: mannwhitneyu_NumHDonors.csv (deflated 11%)\n",
            "updating: mannwhitneyu_pIC50.csv (deflated 14%)\n",
            "updating: descriptors_output.csv (deflated 96%)\n",
            "updating: HIV1_integrase_inhibitor_bioactivity_data_3class_pIC50_pubchem_fp_PaDEL.csv (deflated 96%)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exploartory data analysis\n",
        "\n"
      ],
      "metadata": {
        "id": "h9TB4NkcDR6R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#installing miniconda and rdkit, then importing\n",
        "! wget https://repo.anaconda.com/miniconda/Miniconda3-py37_4.8.2-Linux-x86_64.sh\n",
        "! chmod +x Miniconda3-py37_4.8.2-Linux-x86_64.sh\n",
        "! bash ./Miniconda3-py37_4.8.2-Linux-x86_64.sh -b -f -p /usr/local\n",
        "! conda install -c rdkit rdkit -y\n",
        "import sys\n",
        "sys.path.append('/usr/local/lib/python3.7/site-packages/')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jyw__H1NDl1h",
        "outputId": "f6ff6fa1-9cf2-4c9a-b1d6-91b387b5bb24"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-01-14 11:54:46--  https://repo.anaconda.com/miniconda/Miniconda3-py37_4.8.2-Linux-x86_64.sh\n",
            "Resolving repo.anaconda.com (repo.anaconda.com)... 104.16.131.3, 104.16.130.3, 2606:4700::6810:8303, ...\n",
            "Connecting to repo.anaconda.com (repo.anaconda.com)|104.16.131.3|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 85055499 (81M) [application/x-sh]\n",
            "Saving to: ‘Miniconda3-py37_4.8.2-Linux-x86_64.sh.7’\n",
            "\n",
            "Miniconda3-py37_4.8 100%[===================>]  81.12M  41.0MB/s    in 2.0s    \n",
            "\n",
            "2022-01-14 11:54:48 (41.0 MB/s) - ‘Miniconda3-py37_4.8.2-Linux-x86_64.sh.7’ saved [85055499/85055499]\n",
            "\n",
            "PREFIX=/usr/local\n",
            "Unpacking payload ...\n",
            "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\bdone\n",
            "Solving environment: - \b\b\\ \b\bdone\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local\n",
            "\n",
            "  added / updated specs:\n",
            "    - _libgcc_mutex==0.1=main\n",
            "    - asn1crypto==1.3.0=py37_0\n",
            "    - ca-certificates==2020.1.1=0\n",
            "    - certifi==2019.11.28=py37_0\n",
            "    - cffi==1.14.0=py37h2e261b9_0\n",
            "    - chardet==3.0.4=py37_1003\n",
            "    - conda-package-handling==1.6.0=py37h7b6447c_0\n",
            "    - conda==4.8.2=py37_0\n",
            "    - cryptography==2.8=py37h1ba5d50_0\n",
            "    - idna==2.8=py37_0\n",
            "    - ld_impl_linux-64==2.33.1=h53a641e_7\n",
            "    - libedit==3.1.20181209=hc058e9b_0\n",
            "    - libffi==3.2.1=hd88cf55_4\n",
            "    - libgcc-ng==9.1.0=hdf63c60_0\n",
            "    - libstdcxx-ng==9.1.0=hdf63c60_0\n",
            "    - ncurses==6.2=he6710b0_0\n",
            "    - openssl==1.1.1d=h7b6447c_4\n",
            "    - pip==20.0.2=py37_1\n",
            "    - pycosat==0.6.3=py37h7b6447c_0\n",
            "    - pycparser==2.19=py37_0\n",
            "    - pyopenssl==19.1.0=py37_0\n",
            "    - pysocks==1.7.1=py37_0\n",
            "    - python==3.7.6=h0371630_2\n",
            "    - readline==7.0=h7b6447c_5\n",
            "    - requests==2.22.0=py37_1\n",
            "    - ruamel_yaml==0.15.87=py37h7b6447c_0\n",
            "    - setuptools==45.2.0=py37_0\n",
            "    - six==1.14.0=py37_0\n",
            "    - sqlite==3.31.1=h7b6447c_0\n",
            "    - tk==8.6.8=hbc83047_0\n",
            "    - tqdm==4.42.1=py_0\n",
            "    - urllib3==1.25.8=py37_0\n",
            "    - wheel==0.34.2=py37_0\n",
            "    - xz==5.2.4=h14c3975_4\n",
            "    - yaml==0.1.7=had09818_2\n",
            "    - zlib==1.2.11=h7b6447c_3\n",
            "\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  _libgcc_mutex      pkgs/main/linux-64::_libgcc_mutex-0.1-main\n",
            "  asn1crypto         pkgs/main/linux-64::asn1crypto-1.3.0-py37_0\n",
            "  ca-certificates    pkgs/main/linux-64::ca-certificates-2020.1.1-0\n",
            "  certifi            pkgs/main/linux-64::certifi-2019.11.28-py37_0\n",
            "  cffi               pkgs/main/linux-64::cffi-1.14.0-py37h2e261b9_0\n",
            "  chardet            pkgs/main/linux-64::chardet-3.0.4-py37_1003\n",
            "  conda              pkgs/main/linux-64::conda-4.8.2-py37_0\n",
            "  conda-package-han~ pkgs/main/linux-64::conda-package-handling-1.6.0-py37h7b6447c_0\n",
            "  cryptography       pkgs/main/linux-64::cryptography-2.8-py37h1ba5d50_0\n",
            "  idna               pkgs/main/linux-64::idna-2.8-py37_0\n",
            "  ld_impl_linux-64   pkgs/main/linux-64::ld_impl_linux-64-2.33.1-h53a641e_7\n",
            "  libedit            pkgs/main/linux-64::libedit-3.1.20181209-hc058e9b_0\n",
            "  libffi             pkgs/main/linux-64::libffi-3.2.1-hd88cf55_4\n",
            "  libgcc-ng          pkgs/main/linux-64::libgcc-ng-9.1.0-hdf63c60_0\n",
            "  libstdcxx-ng       pkgs/main/linux-64::libstdcxx-ng-9.1.0-hdf63c60_0\n",
            "  ncurses            pkgs/main/linux-64::ncurses-6.2-he6710b0_0\n",
            "  openssl            pkgs/main/linux-64::openssl-1.1.1d-h7b6447c_4\n",
            "  pip                pkgs/main/linux-64::pip-20.0.2-py37_1\n",
            "  pycosat            pkgs/main/linux-64::pycosat-0.6.3-py37h7b6447c_0\n",
            "  pycparser          pkgs/main/linux-64::pycparser-2.19-py37_0\n",
            "  pyopenssl          pkgs/main/linux-64::pyopenssl-19.1.0-py37_0\n",
            "  pysocks            pkgs/main/linux-64::pysocks-1.7.1-py37_0\n",
            "  python             pkgs/main/linux-64::python-3.7.6-h0371630_2\n",
            "  readline           pkgs/main/linux-64::readline-7.0-h7b6447c_5\n",
            "  requests           pkgs/main/linux-64::requests-2.22.0-py37_1\n",
            "  ruamel_yaml        pkgs/main/linux-64::ruamel_yaml-0.15.87-py37h7b6447c_0\n",
            "  setuptools         pkgs/main/linux-64::setuptools-45.2.0-py37_0\n",
            "  six                pkgs/main/linux-64::six-1.14.0-py37_0\n",
            "  sqlite             pkgs/main/linux-64::sqlite-3.31.1-h7b6447c_0\n",
            "  tk                 pkgs/main/linux-64::tk-8.6.8-hbc83047_0\n",
            "  tqdm               pkgs/main/noarch::tqdm-4.42.1-py_0\n",
            "  urllib3            pkgs/main/linux-64::urllib3-1.25.8-py37_0\n",
            "  wheel              pkgs/main/linux-64::wheel-0.34.2-py37_0\n",
            "  xz                 pkgs/main/linux-64::xz-5.2.4-h14c3975_4\n",
            "  yaml               pkgs/main/linux-64::yaml-0.1.7-had09818_2\n",
            "  zlib               pkgs/main/linux-64::zlib-1.2.11-h7b6447c_3\n",
            "\n",
            "\n",
            "Preparing transaction: / \b\b- \b\b\\ \b\b| \b\bdone\n",
            "Executing transaction: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n",
            "installation finished.\n",
            "WARNING:\n",
            "    You currently have a PYTHONPATH environment variable set. This may cause\n",
            "    unexpected behavior when running the Python interpreter in Miniconda3.\n",
            "    For best results, please verify that your PYTHONPATH only points to\n",
            "    directories of packages that are compatible with the Python interpreter\n",
            "    in Miniconda3: /usr/local\n",
            "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n",
            "Solving environment: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local\n",
            "\n",
            "  added / updated specs:\n",
            "    - rdkit\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    _openmp_mutex-4.5          |            1_gnu          22 KB\n",
            "    blas-1.0                   |              mkl           6 KB\n",
            "    bottleneck-1.3.2           |   py37heb32a55_1         125 KB\n",
            "    bzip2-1.0.8                |       h7b6447c_0          78 KB\n",
            "    ca-certificates-2021.10.26 |       h06a4308_2         115 KB\n",
            "    cairo-1.16.0               |       hf32fb01_1         1.0 MB\n",
            "    certifi-2021.10.8          |   py37h06a4308_2         151 KB\n",
            "    conda-4.11.0               |   py37h06a4308_0        14.4 MB\n",
            "    fontconfig-2.13.1          |       h6c09931_0         250 KB\n",
            "    freetype-2.11.0            |       h70c0345_0         618 KB\n",
            "    giflib-5.2.1               |       h7b6447c_0          78 KB\n",
            "    glib-2.69.1                |       h5202010_0         1.7 MB\n",
            "    icu-58.2                   |       he6710b0_3        10.5 MB\n",
            "    intel-openmp-2021.4.0      |    h06a4308_3561         4.2 MB\n",
            "    jpeg-9d                    |       h7f8727e_0         232 KB\n",
            "    lcms2-2.12                 |       h3be6417_0         312 KB\n",
            "    libboost-1.73.0            |      h3ff78a5_11        13.9 MB\n",
            "    libffi-3.3                 |       he6710b0_2          50 KB\n",
            "    libgcc-ng-9.3.0            |      h5101ec6_17         4.8 MB\n",
            "    libgomp-9.3.0              |      h5101ec6_17         311 KB\n",
            "    libpng-1.6.37              |       hbc83047_0         278 KB\n",
            "    libtiff-4.2.0              |       h85742a9_0         502 KB\n",
            "    libuuid-1.0.3              |       h7f8727e_2          17 KB\n",
            "    libwebp-1.2.0              |       h89dd481_0         493 KB\n",
            "    libwebp-base-1.2.0         |       h27cfd23_0         437 KB\n",
            "    libxcb-1.14                |       h7b6447c_0         505 KB\n",
            "    libxml2-2.9.12             |       h03d6c58_0         1.2 MB\n",
            "    lz4-c-1.9.3                |       h295c915_1         185 KB\n",
            "    mkl-2021.4.0               |     h06a4308_640       142.6 MB\n",
            "    mkl-service-2.4.0          |   py37h7f8727e_0          56 KB\n",
            "    mkl_fft-1.3.1              |   py37hd3c417c_0         172 KB\n",
            "    mkl_random-1.2.2           |   py37h51133e4_0         287 KB\n",
            "    numexpr-2.8.1              |   py37h6abb31d_0         123 KB\n",
            "    numpy-1.21.2               |   py37h20f2e39_0          23 KB\n",
            "    numpy-base-1.21.2          |   py37h79a1101_0         4.8 MB\n",
            "    olefile-0.46               |           py37_0          50 KB\n",
            "    openssl-1.1.1m             |       h7f8727e_0         2.5 MB\n",
            "    packaging-21.3             |     pyhd3eb1b0_0          36 KB\n",
            "    pandas-1.3.5               |   py37h8c16a72_0         9.3 MB\n",
            "    pcre-8.45                  |       h295c915_0         207 KB\n",
            "    pillow-8.4.0               |   py37h5aabda8_0         644 KB\n",
            "    pixman-0.40.0              |       h7f8727e_1         373 KB\n",
            "    py-boost-1.73.0            |  py37ha9443f7_11         204 KB\n",
            "    pyparsing-3.0.4            |     pyhd3eb1b0_0          81 KB\n",
            "    python-dateutil-2.8.2      |     pyhd3eb1b0_0         233 KB\n",
            "    pytz-2021.3                |     pyhd3eb1b0_0         171 KB\n",
            "    rdkit-2020.09.1.0          |   py37hd50e099_1        25.8 MB  rdkit\n",
            "    xz-5.2.5                   |       h7b6447c_0         341 KB\n",
            "    zstd-1.4.9                 |       haebb681_0         480 KB\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:       244.8 MB\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  _openmp_mutex      pkgs/main/linux-64::_openmp_mutex-4.5-1_gnu\n",
            "  blas               pkgs/main/linux-64::blas-1.0-mkl\n",
            "  bottleneck         pkgs/main/linux-64::bottleneck-1.3.2-py37heb32a55_1\n",
            "  bzip2              pkgs/main/linux-64::bzip2-1.0.8-h7b6447c_0\n",
            "  cairo              pkgs/main/linux-64::cairo-1.16.0-hf32fb01_1\n",
            "  fontconfig         pkgs/main/linux-64::fontconfig-2.13.1-h6c09931_0\n",
            "  freetype           pkgs/main/linux-64::freetype-2.11.0-h70c0345_0\n",
            "  giflib             pkgs/main/linux-64::giflib-5.2.1-h7b6447c_0\n",
            "  glib               pkgs/main/linux-64::glib-2.69.1-h5202010_0\n",
            "  icu                pkgs/main/linux-64::icu-58.2-he6710b0_3\n",
            "  intel-openmp       pkgs/main/linux-64::intel-openmp-2021.4.0-h06a4308_3561\n",
            "  jpeg               pkgs/main/linux-64::jpeg-9d-h7f8727e_0\n",
            "  lcms2              pkgs/main/linux-64::lcms2-2.12-h3be6417_0\n",
            "  libboost           pkgs/main/linux-64::libboost-1.73.0-h3ff78a5_11\n",
            "  libgomp            pkgs/main/linux-64::libgomp-9.3.0-h5101ec6_17\n",
            "  libpng             pkgs/main/linux-64::libpng-1.6.37-hbc83047_0\n",
            "  libtiff            pkgs/main/linux-64::libtiff-4.2.0-h85742a9_0\n",
            "  libuuid            pkgs/main/linux-64::libuuid-1.0.3-h7f8727e_2\n",
            "  libwebp            pkgs/main/linux-64::libwebp-1.2.0-h89dd481_0\n",
            "  libwebp-base       pkgs/main/linux-64::libwebp-base-1.2.0-h27cfd23_0\n",
            "  libxcb             pkgs/main/linux-64::libxcb-1.14-h7b6447c_0\n",
            "  libxml2            pkgs/main/linux-64::libxml2-2.9.12-h03d6c58_0\n",
            "  lz4-c              pkgs/main/linux-64::lz4-c-1.9.3-h295c915_1\n",
            "  mkl                pkgs/main/linux-64::mkl-2021.4.0-h06a4308_640\n",
            "  mkl-service        pkgs/main/linux-64::mkl-service-2.4.0-py37h7f8727e_0\n",
            "  mkl_fft            pkgs/main/linux-64::mkl_fft-1.3.1-py37hd3c417c_0\n",
            "  mkl_random         pkgs/main/linux-64::mkl_random-1.2.2-py37h51133e4_0\n",
            "  numexpr            pkgs/main/linux-64::numexpr-2.8.1-py37h6abb31d_0\n",
            "  numpy              pkgs/main/linux-64::numpy-1.21.2-py37h20f2e39_0\n",
            "  numpy-base         pkgs/main/linux-64::numpy-base-1.21.2-py37h79a1101_0\n",
            "  olefile            pkgs/main/linux-64::olefile-0.46-py37_0\n",
            "  packaging          pkgs/main/noarch::packaging-21.3-pyhd3eb1b0_0\n",
            "  pandas             pkgs/main/linux-64::pandas-1.3.5-py37h8c16a72_0\n",
            "  pcre               pkgs/main/linux-64::pcre-8.45-h295c915_0\n",
            "  pillow             pkgs/main/linux-64::pillow-8.4.0-py37h5aabda8_0\n",
            "  pixman             pkgs/main/linux-64::pixman-0.40.0-h7f8727e_1\n",
            "  py-boost           pkgs/main/linux-64::py-boost-1.73.0-py37ha9443f7_11\n",
            "  pyparsing          pkgs/main/noarch::pyparsing-3.0.4-pyhd3eb1b0_0\n",
            "  python-dateutil    pkgs/main/noarch::python-dateutil-2.8.2-pyhd3eb1b0_0\n",
            "  pytz               pkgs/main/noarch::pytz-2021.3-pyhd3eb1b0_0\n",
            "  rdkit              rdkit/linux-64::rdkit-2020.09.1.0-py37hd50e099_1\n",
            "  zstd               pkgs/main/linux-64::zstd-1.4.9-haebb681_0\n",
            "\n",
            "The following packages will be UPDATED:\n",
            "\n",
            "  ca-certificates                                2020.1.1-0 --> 2021.10.26-h06a4308_2\n",
            "  certifi                                 2019.11.28-py37_0 --> 2021.10.8-py37h06a4308_2\n",
            "  conda                                        4.8.2-py37_0 --> 4.11.0-py37h06a4308_0\n",
            "  libffi                                   3.2.1-hd88cf55_4 --> 3.3-he6710b0_2\n",
            "  libgcc-ng                                9.1.0-hdf63c60_0 --> 9.3.0-h5101ec6_17\n",
            "  openssl                                 1.1.1d-h7b6447c_4 --> 1.1.1m-h7f8727e_0\n",
            "  xz                                       5.2.4-h14c3975_4 --> 5.2.5-h7b6447c_0\n",
            "\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages\n",
            "mkl_random-1.2.2     | 287 KB    | : 100% 1.0/1 [00:00<00:00,  8.39it/s]\n",
            "libwebp-1.2.0        | 493 KB    | : 100% 1.0/1 [00:00<00:00, 11.63it/s]\n",
            "zstd-1.4.9           | 480 KB    | : 100% 1.0/1 [00:00<00:00, 11.46it/s]\n",
            "numpy-1.21.2         | 23 KB     | : 100% 1.0/1 [00:00<00:00, 15.59it/s]\n",
            "libgomp-9.3.0        | 311 KB    | : 100% 1.0/1 [00:00<00:00, 10.75it/s]\n",
            "libxml2-2.9.12       | 1.2 MB    | : 100% 1.0/1 [00:00<00:00,  7.97it/s]\n",
            "olefile-0.46         | 50 KB     | : 100% 1.0/1 [00:00<00:00, 15.59it/s]\n",
            "lz4-c-1.9.3          | 185 KB    | : 100% 1.0/1 [00:00<00:00, 15.99it/s]\n",
            "fontconfig-2.13.1    | 250 KB    | : 100% 1.0/1 [00:00<00:00, 14.08it/s]\n",
            "pillow-8.4.0         | 644 KB    | : 100% 1.0/1 [00:00<00:00, 10.75it/s]\n",
            "blas-1.0             | 6 KB      | : 100% 1.0/1 [00:00<00:00, 16.85it/s]\n",
            "numpy-base-1.21.2    | 4.8 MB    | : 100% 1.0/1 [00:00<00:00,  3.16it/s]\n",
            "rdkit-2020.09.1.0    | 25.8 MB   | : 100% 1.0/1 [00:07<00:00,  7.00s/it]\n",
            "_openmp_mutex-4.5    | 22 KB     | : 100% 1.0/1 [00:00<00:00, 29.37it/s]\n",
            "libgcc-ng-9.3.0      | 4.8 MB    | : 100% 1.0/1 [00:00<00:00,  4.82it/s]\n",
            "mkl-service-2.4.0    | 56 KB     | : 100% 1.0/1 [00:00<00:00, 15.59it/s]\n",
            "libxcb-1.14          | 505 KB    | : 100% 1.0/1 [00:00<00:00, 10.86it/s]\n",
            "libpng-1.6.37        | 278 KB    | : 100% 1.0/1 [00:00<00:00, 12.67it/s]\n",
            "glib-2.69.1          | 1.7 MB    | : 100% 1.0/1 [00:00<00:00,  6.83it/s]\n",
            "icu-58.2             | 10.5 MB   | : 100% 1.0/1 [00:00<00:00,  1.70it/s]               \n",
            "cairo-1.16.0         | 1.0 MB    | : 100% 1.0/1 [00:00<00:00,  8.61it/s]\n",
            "conda-4.11.0         | 14.4 MB   | : 100% 1.0/1 [00:01<00:00,  1.10it/s]               \n",
            "mkl_fft-1.3.1        | 172 KB    | : 100% 1.0/1 [00:00<00:00, 12.86it/s]\n",
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            "libffi-3.3           | 50 KB     | : 100% 1.0/1 [00:00<00:00, 10.82it/s]\n",
            "lcms2-2.12           | 312 KB    | : 100% 1.0/1 [00:00<00:00, 13.53it/s]\n",
            "intel-openmp-2021.4. | 4.2 MB    | : 100% 1.0/1 [00:00<00:00,  4.04it/s]\n",
            "openssl-1.1.1m       | 2.5 MB    | : 100% 1.0/1 [00:00<00:00,  4.66it/s]\n",
            "py-boost-1.73.0      | 204 KB    | : 100% 1.0/1 [00:00<00:00, 11.30it/s]\n",
            "ca-certificates-2021 | 115 KB    | : 100% 1.0/1 [00:00<00:00, 15.34it/s]\n",
            "pytz-2021.3          | 171 KB    | : 100% 1.0/1 [00:00<00:00,  8.24it/s]\n",
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            "mkl-2021.4.0         | 142.6 MB  | : 100% 1.0/1 [00:13<00:00, 13.26s/it]               \n",
            "pyparsing-3.0.4      | 81 KB     | : 100% 1.0/1 [00:00<00:00,  9.87it/s]\n",
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            "pixman-0.40.0        | 373 KB    | : 100% 1.0/1 [00:00<00:00,  8.09it/s]\n",
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            "bzip2-1.0.8          | 78 KB     | : 100% 1.0/1 [00:00<00:00, 13.30it/s]\n",
            "Preparing transaction: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n",
            "Verifying transaction: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n",
            "Executing transaction: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#importing data and renaming the classifier column\n",
        "data = pd.read_csv('HIV1_integrase_inhibitor_bioactivity_data_curated.csv')\n",
        "data = data.rename(columns={\"0\":\"class\"}, errors=\"raise\")\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "h9AP6NBgm1tD",
        "outputId": "8f3194f5-23c3-4b41-8145-213f578b7783"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>molecule_chembl_id</th>\n",
              "      <th>canonical_smiles</th>\n",
              "      <th>standard_value</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CHEMBL324842</td>\n",
              "      <td>O=C(/C=C/c1ccc(O)c(O)c1)O[C@H](Cc1ccc(O)c(O)c1...</td>\n",
              "      <td>9000.0</td>\n",
              "      <td>intermediate</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CHEMBL304722</td>\n",
              "      <td>O=C(CC(O)CCc1ccc(O)c(O)c1)O[C@H]1Cc2cc(O)c(O)c...</td>\n",
              "      <td>1400.0</td>\n",
              "      <td>intermediate</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CHEMBL67076</td>\n",
              "      <td>O=C(CCc1ccc(O)c(O)c1)c1ccc(O)c(O)c1O</td>\n",
              "      <td>1700.0</td>\n",
              "      <td>intermediate</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CHEMBL177126</td>\n",
              "      <td>O=C(/C=C/c1ccc(O)c(O)c1)O[C@H]1[C@H](O)C[C@](O...</td>\n",
              "      <td>250.0</td>\n",
              "      <td>active</td>\n",
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              "      <th>4</th>\n",
              "      <td>CHEMBL145335</td>\n",
              "      <td>O=C(O)C(=O)CC(=O)c1cccc(Cc2cccc(Cl)c2)c1</td>\n",
              "      <td>100.0</td>\n",
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              "      <th>...</th>\n",
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              "      <th>4067</th>\n",
              "      <td>CHEMBL4649015</td>\n",
              "      <td>CN1CCN(c2ccc3nc(-c4ccc5nc(CNC(=O)CCCCN6CCN(CCC...</td>\n",
              "      <td>3900.0</td>\n",
              "      <td>intermediate</td>\n",
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              "      <th>4068</th>\n",
              "      <td>CHEMBL4640673</td>\n",
              "      <td>CN(C)CCCNC(=O)c1ccc2nc(-c3ccc4nc(CNC(=O)CN5CCN...</td>\n",
              "      <td>2100.0</td>\n",
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              "    <tr>\n",
              "      <th>4069</th>\n",
              "      <td>CHEMBL4633363</td>\n",
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              "      <td>15000.0</td>\n",
              "      <td>inactive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4070</th>\n",
              "      <td>CHEMBL4644631</td>\n",
              "      <td>CN1CCN(c2ccc3nc(-c4ccc5nc(CNC(=O)CCN6CCN(CCC(=...</td>\n",
              "      <td>7000.0</td>\n",
              "      <td>intermediate</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4071</th>\n",
              "      <td>CHEMBL4645067</td>\n",
              "      <td>CN1CCN(c2ccc3nc(-c4ccc5nc(CNC(=O)CCCN6CCN(CCCC...</td>\n",
              "      <td>9900.0</td>\n",
              "      <td>intermediate</td>\n",
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              "  </tbody>\n",
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              "<p>4072 rows × 4 columns</p>\n",
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            ],
            "text/plain": [
              "     molecule_chembl_id  ...         class\n",
              "0          CHEMBL324842  ...  intermediate\n",
              "1          CHEMBL304722  ...  intermediate\n",
              "2           CHEMBL67076  ...  intermediate\n",
              "3          CHEMBL177126  ...        active\n",
              "4          CHEMBL145335  ...        active\n",
              "...                 ...  ...           ...\n",
              "4067      CHEMBL4649015  ...  intermediate\n",
              "4068      CHEMBL4640673  ...  intermediate\n",
              "4069      CHEMBL4633363  ...      inactive\n",
              "4070      CHEMBL4644631  ...  intermediate\n",
              "4071      CHEMBL4645067  ...  intermediate\n",
              "\n",
              "[4072 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#preparing a list of smiles (excluding bound ions) for calculation of Lipinski descriptors\n",
        "smiles = []\n",
        "\n",
        "for i in data.canonical_smiles.tolist():\n",
        "  split_smile = str(i).split(\".\")\n",
        "  longest_mol = max(split_smile, key = len)\n",
        "  smiles.append(longest_mol)\n",
        "\n",
        "smiles = pd.Series(smiles, name = \"canonical_smiles\")\n",
        "smiles"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xlQgEr0lILrO",
        "outputId": "2a87bb7f-2c24-4262-b32c-5be2f7dbca8e"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       O=C(/C=C/c1ccc(O)c(O)c1)O[C@H](Cc1ccc(O)c(O)c1...\n",
              "1       O=C(CC(O)CCc1ccc(O)c(O)c1)O[C@H]1Cc2cc(O)c(O)c...\n",
              "2                    O=C(CCc1ccc(O)c(O)c1)c1ccc(O)c(O)c1O\n",
              "3       O=C(/C=C/c1ccc(O)c(O)c1)O[C@H]1[C@H](O)C[C@](O...\n",
              "4                O=C(O)C(=O)CC(=O)c1cccc(Cc2cccc(Cl)c2)c1\n",
              "                              ...                        \n",
              "4067    CN1CCN(c2ccc3nc(-c4ccc5nc(CNC(=O)CCCCN6CCN(CCC...\n",
              "4068    CN(C)CCCNC(=O)c1ccc2nc(-c3ccc4nc(CNC(=O)CN5CCN...\n",
              "4069    CN1CCN(c2ccc3nc(-c4ccc5nc(CNC(=O)CN6CCN(CC(=O)...\n",
              "4070    CN1CCN(c2ccc3nc(-c4ccc5nc(CNC(=O)CCN6CCN(CCC(=...\n",
              "4071    CN1CCN(c2ccc3nc(-c4ccc5nc(CNC(=O)CCCN6CCN(CCCC...\n",
              "Name: canonical_smiles, Length: 4072, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#importing modules required for calculation of Lipinski descriptors\n",
        "import numpy as np\n",
        "from rdkit import Chem\n",
        "from rdkit.Chem import Descriptors, Lipinski"
      ],
      "metadata": {
        "id": "G6TVPITwP9oF"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#calculating Lipinski descriptors\n",
        "# Inspired by: https://codeocean.com/explore/capsules?query=tag:data-curation and https://www.youtube.com/watch?v=jBlTQjcKuaY\n",
        "\n",
        "def lipinski(smiles, verbose=False):\n",
        "\n",
        "    moldata= []\n",
        "    for elem in smiles:\n",
        "        mol=Chem.MolFromSmiles(elem) \n",
        "        moldata.append(mol)\n",
        "       \n",
        "    baseData= np.arange(1,1)\n",
        "    i=0  \n",
        "    for mol in moldata:        \n",
        "       \n",
        "        desc_MolWt = Descriptors.MolWt(mol)\n",
        "        desc_MolLogP = Descriptors.MolLogP(mol)\n",
        "        desc_NumHDonors = Lipinski.NumHDonors(mol)\n",
        "        desc_NumHAcceptors = Lipinski.NumHAcceptors(mol)\n",
        "           \n",
        "        row = np.array([desc_MolWt,\n",
        "                        desc_MolLogP,\n",
        "                        desc_NumHDonors,\n",
        "                        desc_NumHAcceptors])   \n",
        "    \n",
        "        if(i==0):\n",
        "            baseData=row\n",
        "        else:\n",
        "            baseData=np.vstack([baseData, row])\n",
        "        i=i+1      \n",
        "    \n",
        "    columnNames=[\"MW\",\"LogP\",\"NumHDonors\",\"NumHAcceptors\"]   \n",
        "    descriptors = pd.DataFrame(data=baseData,columns=columnNames)\n",
        "    \n",
        "    return descriptors\n",
        "\n",
        "df_lipinski = lipinski(smiles)\n",
        "df_lipinski"
      ],
      "metadata": {
        "id": "qAaGApGoI8a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "39d575ca-e5e0-4244-b0c1-002902b8a9e9"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>NumHAcceptors</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>360.318</td>\n",
              "      <td>1.7613</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7.0</td>\n",
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              "      <th>1</th>\n",
              "      <td>498.484</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>516.455</td>\n",
              "      <td>1.0296</td>\n",
              "      <td>7.0</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>316.740</td>\n",
              "      <td>3.1573</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4067</th>\n",
              "      <td>973.248</td>\n",
              "      <td>5.9202</td>\n",
              "      <td>6.0</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4068</th>\n",
              "      <td>949.138</td>\n",
              "      <td>3.4390</td>\n",
              "      <td>8.0</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4069</th>\n",
              "      <td>889.086</td>\n",
              "      <td>3.5796</td>\n",
              "      <td>6.0</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4070</th>\n",
              "      <td>917.140</td>\n",
              "      <td>4.3598</td>\n",
              "      <td>6.0</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4071</th>\n",
              "      <td>945.194</td>\n",
              "      <td>5.1400</td>\n",
              "      <td>6.0</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4072 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f9d61b6d-a5d4-4644-a710-8744fc117ad2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f9d61b6d-a5d4-4644-a710-8744fc117ad2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "           MW    LogP  NumHDonors  NumHAcceptors\n",
              "0     360.318  1.7613         5.0            7.0\n",
              "1     498.484  2.8920         7.0           10.0\n",
              "2     290.271  2.0301         5.0            6.0\n",
              "3     516.455  1.0296         7.0           11.0\n",
              "4     316.740  3.1573         1.0            3.0\n",
              "...       ...     ...         ...            ...\n",
              "4067  973.248  5.9202         6.0           12.0\n",
              "4068  949.138  3.4390         8.0           12.0\n",
              "4069  889.086  3.5796         6.0           12.0\n",
              "4070  917.140  4.3598         6.0           12.0\n",
              "4071  945.194  5.1400         6.0           12.0\n",
              "\n",
              "[4072 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#merging the dataframes\n",
        "df_no_smiles = data.drop(columns=\"canonical_smiles\")\n",
        "df_combined = pd.concat([df_no_smiles, smiles, df_lipinski], axis = 1)\n",
        "df_combined"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "YSLSgtTBRrqZ",
        "outputId": "86bd7698-408e-4242-ca5f-9ddace1887fe"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-f0af2758-e5d2-402d-856c-1660f687ea12\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>molecule_chembl_id</th>\n",
              "      <th>standard_value</th>\n",
              "      <th>class</th>\n",
              "      <th>canonical_smiles</th>\n",
              "      <th>MW</th>\n",
              "      <th>LogP</th>\n",
              "      <th>NumHDonors</th>\n",
              "      <th>NumHAcceptors</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CHEMBL324842</td>\n",
              "      <td>9000.0</td>\n",
              "      <td>intermediate</td>\n",
              "      <td>O=C(/C=C/c1ccc(O)c(O)c1)O[C@H](Cc1ccc(O)c(O)c1...</td>\n",
              "      <td>360.318</td>\n",
              "      <td>1.7613</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CHEMBL304722</td>\n",
              "      <td>1400.0</td>\n",
              "      <td>intermediate</td>\n",
              "      <td>O=C(CC(O)CCc1ccc(O)c(O)c1)O[C@H]1Cc2cc(O)c(O)c...</td>\n",
              "      <td>498.484</td>\n",
              "      <td>2.8920</td>\n",
              "      <td>7.0</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CHEMBL67076</td>\n",
              "      <td>1700.0</td>\n",
              "      <td>intermediate</td>\n",
              "      <td>O=C(CCc1ccc(O)c(O)c1)c1ccc(O)c(O)c1O</td>\n",
              "      <td>290.271</td>\n",
              "      <td>2.0301</td>\n",
              "      <td>5.0</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CHEMBL177126</td>\n",
              "      <td>250.0</td>\n",
              "      <td>active</td>\n",
              "      <td>O=C(/C=C/c1ccc(O)c(O)c1)O[C@H]1[C@H](O)C[C@](O...</td>\n",
              "      <td>516.455</td>\n",
              "      <td>1.0296</td>\n",
              "      <td>7.0</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CHEMBL145335</td>\n",
              "      <td>100.0</td>\n",
              "      <td>active</td>\n",
              "      <td>O=C(O)C(=O)CC(=O)c1cccc(Cc2cccc(Cl)c2)c1</td>\n",
              "      <td>316.740</td>\n",
              "      <td>3.1573</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4067</th>\n",
              "      <td>CHEMBL4649015</td>\n",
              "      <td>3900.0</td>\n",
              "      <td>intermediate</td>\n",
              "      <td>CN1CCN(c2ccc3nc(-c4ccc5nc(CNC(=O)CCCCN6CCN(CCC...</td>\n",
              "      <td>973.248</td>\n",
              "      <td>5.9202</td>\n",
              "      <td>6.0</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4068</th>\n",
              "      <td>CHEMBL4640673</td>\n",
              "      <td>2100.0</td>\n",
              "      <td>intermediate</td>\n",
              "      <td>CN(C)CCCNC(=O)c1ccc2nc(-c3ccc4nc(CNC(=O)CN5CCN...</td>\n",
              "      <td>949.138</td>\n",
              "      <td>3.4390</td>\n",
              "      <td>8.0</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4069</th>\n",
              "      <td>CHEMBL4633363</td>\n",
              "      <td>15000.0</td>\n",
              "      <td>inactive</td>\n",
              "      <td>CN1CCN(c2ccc3nc(-c4ccc5nc(CNC(=O)CN6CCN(CC(=O)...</td>\n",
              "      <td>889.086</td>\n",
              "      <td>3.5796</td>\n",
              "      <td>6.0</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4070</th>\n",
              "      <td>CHEMBL4644631</td>\n",
              "      <td>7000.0</td>\n",
              "      <td>intermediate</td>\n",
              "      <td>CN1CCN(c2ccc3nc(-c4ccc5nc(CNC(=O)CCN6CCN(CCC(=...</td>\n",
              "      <td>917.140</td>\n",
              "      <td>4.3598</td>\n",
              "      <td>6.0</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4071</th>\n",
              "      <td>CHEMBL4645067</td>\n",
              "      <td>9900.0</td>\n",
              "      <td>intermediate</td>\n",
              "      <td>CN1CCN(c2ccc3nc(-c4ccc5nc(CNC(=O)CCCN6CCN(CCCC...</td>\n",
              "      <td>945.194</td>\n",
              "      <td>5.1400</td>\n",
              "      <td>6.0</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4072 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f0af2758-e5d2-402d-856c-1660f687ea12')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f0af2758-e5d2-402d-856c-1660f687ea12 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f0af2758-e5d2-402d-856c-1660f687ea12');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     molecule_chembl_id  standard_value  ... NumHDonors NumHAcceptors\n",
              "0          CHEMBL324842          9000.0  ...        5.0           7.0\n",
              "1          CHEMBL304722          1400.0  ...        7.0          10.0\n",
              "2           CHEMBL67076          1700.0  ...        5.0           6.0\n",
              "3          CHEMBL177126           250.0  ...        7.0          11.0\n",
              "4          CHEMBL145335           100.0  ...        1.0           3.0\n",
              "...                 ...             ...  ...        ...           ...\n",
              "4067      CHEMBL4649015          3900.0  ...        6.0          12.0\n",
              "4068      CHEMBL4640673          2100.0  ...        8.0          12.0\n",
              "4069      CHEMBL4633363         15000.0  ...        6.0          12.0\n",
              "4070      CHEMBL4644631          7000.0  ...        6.0          12.0\n",
              "4071      CHEMBL4645067          9900.0  ...        6.0          12.0\n",
              "\n",
              "[4072 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#function to convert IC50 (standard_value) to pIC50 (negative log scale) to make the distribution more uniform\n",
        "#function code taken from: https://github.com/chaninlab/estrogen-receptor-alpha-qsar/blob/master/02_ER_alpha_RO5.ipynb\n",
        "\n",
        "def pIC50(input):\n",
        "    pIC50 = []\n",
        "\n",
        "    for i in input['standard_value_norm']:\n",
        "        molar = i*(10**-9) # Converts nM to M\n",
        "        pIC50.append(-np.log10(molar))\n",
        "\n",
        "    input['pIC50'] = pIC50\n",
        "    x = input.drop('standard_value_norm', 1)\n",
        "        \n",
        "    return x"
      ],
      "metadata": {
        "id": "RJgFGDE0V4g0"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#function to normalise standard_value column to avoid positive values in the pIC50 function\n",
        "def norm_value(input):\n",
        "    norm = []\n",
        "\n",
        "    for i in input['standard_value']:\n",
        "        if i > 100000000:\n",
        "          i = 100000000\n",
        "        norm.append(i)\n",
        "\n",
        "    input['standard_value_norm'] = norm\n",
        "    x = input.drop('standard_value', 1)\n",
        "        \n",
        "    return x"
      ],
      "metadata": {
        "id": "ypPH_ToYejhm"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#standard_value normalisation\n",
        "df_norm = norm_value(df_combined)\n",
        "df_norm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        },
        "id": "9_2qb7ahgV4a",
        "outputId": "82722524-57ad-44ef-b4b0-4560785a5d0d"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>molecule_chembl_id</th>\n",
              "      <th>class</th>\n",
              "      <th>canonical_smiles</th>\n",
              "      <th>MW</th>\n",
              "      <th>LogP</th>\n",
              "      <th>NumHDonors</th>\n",
              "      <th>NumHAcceptors</th>\n",
              "      <th>standard_value_norm</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CHEMBL324842</td>\n",
              "      <td>intermediate</td>\n",
              "      <td>O=C(/C=C/c1ccc(O)c(O)c1)O[C@H](Cc1ccc(O)c(O)c1...</td>\n",
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              "      <td>1400.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CHEMBL67076</td>\n",
              "      <td>intermediate</td>\n",
              "      <td>O=C(CCc1ccc(O)c(O)c1)c1ccc(O)c(O)c1O</td>\n",
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              "      <td>2.0301</td>\n",
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              "      <td>6.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CHEMBL177126</td>\n",
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              "      <td>O=C(/C=C/c1ccc(O)c(O)c1)O[C@H]1[C@H](O)C[C@](O...</td>\n",
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              "      <td>11.0</td>\n",
              "      <td>250.0</td>\n",
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              "    <tr>\n",
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              "      <td>CHEMBL145335</td>\n",
              "      <td>active</td>\n",
              "      <td>O=C(O)C(=O)CC(=O)c1cccc(Cc2cccc(Cl)c2)c1</td>\n",
              "      <td>316.740</td>\n",
              "      <td>3.1573</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>100.0</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>4067</th>\n",
              "      <td>CHEMBL4649015</td>\n",
              "      <td>intermediate</td>\n",
              "      <td>CN1CCN(c2ccc3nc(-c4ccc5nc(CNC(=O)CCCCN6CCN(CCC...</td>\n",
              "      <td>973.248</td>\n",
              "      <td>5.9202</td>\n",
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              "    <tr>\n",
              "      <th>4068</th>\n",
              "      <td>CHEMBL4640673</td>\n",
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              "      <td>CN(C)CCCNC(=O)c1ccc2nc(-c3ccc4nc(CNC(=O)CN5CCN...</td>\n",
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              "      <th>4069</th>\n",
              "      <td>CHEMBL4633363</td>\n",
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              "      <td>CN1CCN(c2ccc3nc(-c4ccc5nc(CNC(=O)CN6CCN(CC(=O)...</td>\n",
              "      <td>889.086</td>\n",
              "      <td>3.5796</td>\n",
              "      <td>6.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>15000.0</td>\n",
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              "      <th>4070</th>\n",
              "      <td>CHEMBL4644631</td>\n",
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              "      <td>CN1CCN(c2ccc3nc(-c4ccc5nc(CNC(=O)CCN6CCN(CCC(=...</td>\n",
              "      <td>917.140</td>\n",
              "      <td>4.3598</td>\n",
              "      <td>6.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>7000.0</td>\n",
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              "      <th>4071</th>\n",
              "      <td>CHEMBL4645067</td>\n",
              "      <td>intermediate</td>\n",
              "      <td>CN1CCN(c2ccc3nc(-c4ccc5nc(CNC(=O)CCCN6CCN(CCCC...</td>\n",
              "      <td>945.194</td>\n",
              "      <td>5.1400</td>\n",
              "      <td>6.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>9900.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4072 rows × 8 columns</p>\n",
              "</div>\n",
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              "\n",
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              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-781394b2-4e2a-4b6d-8d89-444db6626345');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     molecule_chembl_id         class  ... NumHAcceptors  standard_value_norm\n",
              "0          CHEMBL324842  intermediate  ...           7.0               9000.0\n",
              "1          CHEMBL304722  intermediate  ...          10.0               1400.0\n",
              "2           CHEMBL67076  intermediate  ...           6.0               1700.0\n",
              "3          CHEMBL177126        active  ...          11.0                250.0\n",
              "4          CHEMBL145335        active  ...           3.0                100.0\n",
              "...                 ...           ...  ...           ...                  ...\n",
              "4067      CHEMBL4649015  intermediate  ...          12.0               3900.0\n",
              "4068      CHEMBL4640673  intermediate  ...          12.0               2100.0\n",
              "4069      CHEMBL4633363      inactive  ...          12.0              15000.0\n",
              "4070      CHEMBL4644631  intermediate  ...          12.0               7000.0\n",
              "4071      CHEMBL4645067  intermediate  ...          12.0               9900.0\n",
              "\n",
              "[4072 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#IC50 to pIC50 conversion\n",
        "df_final = pIC50(df_norm)\n",
        "df_final"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "vhRS9z0Ugbut",
        "outputId": "b5ada926-4998-4571-c4c0-618f2cf9d692"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "      <th></th>\n",
              "      <th>molecule_chembl_id</th>\n",
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              "      <th>canonical_smiles</th>\n",
              "      <th>MW</th>\n",
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              "      <th>NumHAcceptors</th>\n",
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              "      <th>0</th>\n",
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              "      <td>CHEMBL67076</td>\n",
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              "      <td>O=C(CCc1ccc(O)c(O)c1)c1ccc(O)c(O)c1O</td>\n",
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              "      <th>3</th>\n",
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              "      <td>11.0</td>\n",
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              "      <td>CHEMBL145335</td>\n",
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              "      <td>O=C(O)C(=O)CC(=O)c1cccc(Cc2cccc(Cl)c2)c1</td>\n",
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              "      <td>...</td>\n",
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              "      <th>4067</th>\n",
              "      <td>CHEMBL4649015</td>\n",
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              "      <td>CN1CCN(c2ccc3nc(-c4ccc5nc(CNC(=O)CCCCN6CCN(CCC...</td>\n",
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              "      <td>CHEMBL4640673</td>\n",
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              "      <td>CN(C)CCCNC(=O)c1ccc2nc(-c3ccc4nc(CNC(=O)CN5CCN...</td>\n",
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              "      <td>CHEMBL4633363</td>\n",
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              "      <td>CHEMBL4644631</td>\n",
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              "      <td>917.140</td>\n",
              "      <td>4.3598</td>\n",
              "      <td>6.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>5.154902</td>\n",
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              "      <th>4071</th>\n",
              "      <td>CHEMBL4645067</td>\n",
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              "      <td>CN1CCN(c2ccc3nc(-c4ccc5nc(CNC(=O)CCCN6CCN(CCCC...</td>\n",
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              "      <td>12.0</td>\n",
              "      <td>5.004365</td>\n",
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              "<p>4072 rows × 8 columns</p>\n",
              "</div>\n",
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              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-bd22e60b-27d2-43b8-811d-a1c354f55fed button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bd22e60b-27d2-43b8-811d-a1c354f55fed');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     molecule_chembl_id         class  ... NumHAcceptors     pIC50\n",
              "0          CHEMBL324842  intermediate  ...           7.0  5.045757\n",
              "1          CHEMBL304722  intermediate  ...          10.0  5.853872\n",
              "2           CHEMBL67076  intermediate  ...           6.0  5.769551\n",
              "3          CHEMBL177126        active  ...          11.0  6.602060\n",
              "4          CHEMBL145335        active  ...           3.0  7.000000\n",
              "...                 ...           ...  ...           ...       ...\n",
              "4067      CHEMBL4649015  intermediate  ...          12.0  5.408935\n",
              "4068      CHEMBL4640673  intermediate  ...          12.0  5.677781\n",
              "4069      CHEMBL4633363      inactive  ...          12.0  4.823909\n",
              "4070      CHEMBL4644631  intermediate  ...          12.0  5.154902\n",
              "4071      CHEMBL4645067  intermediate  ...          12.0  5.004365\n",
              "\n",
              "[4072 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#saving data to csv with 2 (active/inactive) and 3 (inclusive of intermediate) classifiers\n",
        "\n",
        "#3 class\n",
        "df_final.to_csv(\"HIV1_integrase_inhibitor_3class_pIC50.csv\")\n",
        "\n",
        "#2 class\n",
        "df_final_2class = df_final[df_final['class'] != 'intermediate']\n",
        "df_final_2class.to_csv(\"HIV1_integrase_inhibitor_2class_pIC50.csv\")"
      ],
      "metadata": {
        "id": "Z8TeuDkucSu5"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#importing data visualisation packages\n",
        "import seaborn as sns\n",
        "sns.set(style='ticks')\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "n4wJ9WfNvuSZ"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#frequency plot of active/inactive compounds\n",
        "sns.countplot(x=\"class\", data = df_final_2class, edgecolor = \"black\")\n",
        "\n",
        "plt.xlabel(\"Bioactivity class\", fontsize = 14, fontweight = \"bold\")\n",
        "plt.ylabel(\"Frequency\", fontsize = 14, fontweight = \"bold\")\n",
        "plt.savefig(\"plot_bioactivity_class.pdf\")"
      ],
      "metadata": {
        "id": "MsAQ5f4v2RBv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "e3ce7f0b-9466-4251-eb83-b70ae2a37233"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#creating a scatter plot of Molecular Weight vs LogP\n",
        "plt.figure(figsize=(6, 6))\n",
        "\n",
        "sns.scatterplot(x=\"MW\", y=\"LogP\", data = df_final_2class, hue = \"class\", size = \"pIC50\", edgecolor = \"black\", alpha = 0.7)\n",
        "\n",
        "plt.xlabel(\"MW\", fontsize = 14, fontweight = \"bold\")\n",
        "plt.ylabel(\"LogP\", fontsize = 14, fontweight = \"bold\")\n",
        "plt.legend(bbox_to_anchor = (1.05, 1), loc=2, borderaxespad=0)\n",
        "plt.savefig(\"plot_MW_vs_LogP.pdf\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "id": "Ih43hf9ohGGE",
        "outputId": "70965810-7da1-481c-a14e-e954cce61b4d"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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l2rRpfPjhhzzyyCO1Gq9b3UMHePPNNzly5AgLFiwoX+y+Xbt2mM1m9u7dC1z6AAcPHlzl+/38/IiKiqrwJyIios7iF0II4d7i4uJ45513SEhIYNCgQWzYsKHKevPnz+e5554rT+YA3bp1o0OHDn/a/o4dO2jXrl35+0aNGsW6detqKvzLcqsr9FOnTrFo0SJiY2MZNWoUcGn3mwULFpCYmMj06dMrPLYmhBBC/B0qlYpVq1Zx9uxZ7rnnHrp06VLhUejc3FwyMzPp2LHjn7Yzbtw47HY7vXv35tFHH0Wr1Vaa9xUZGUlGRkatnctv3Cqht2jRghMnTlR5rHPnziQlJdVxREIIIRqikSNHAtC0aVPatGnDgQMH6N+//1W1sW3bNgwGA8XFxTz99NMsWLCAJ554ojbCvSJuN+QuhBBCuFpwcDDh4eEcOnTosnUMBgMAPj4+jBw5kn379pWX/3HeV3p6ennd2iQJXQghxDVnxYoVAJw/f55ff/2VTp06Varz0EMP8dprr5GSklJetmfPHg4dOkRhYSFmsxkAm83Ghg0byjdN6dWrF4cPHy7fXnXZsmUMGTKkls/IzYbchRBCiLpgt9sZPnw4JpOJWbNmVbmU+KhRo9Dr9Tz22GOYzWZUKhWtWrXi6aef5uzZs7z44osoioLNZiM+Pp7HH38cuHTFPmvWLB544AEcDgetW7dm2rRptX5Osn2qEEKIKrly+9TaFBcXx759+yo9blYf/NnnKUPuQgghRAMgQ+5CCCGuKZd7mqq+kyt0IYQQogGQhC6EEEI0AJLQhRBCiAZAEroQQgjRAEhCF0IIIRoASehCCCGuKXPmzKFfv37ExcVx8uTJKuvMmzePHj16kJCQQEJCAjNnzqzjKK+ePLYmhBDimtK/f3/Gjx/PmDFj/rTe8OHDmTp1ah1FVX2S0IUQQril1NRULly4QHR0dI2u8tmlS5caa8udSEIXQgjhdpYuXcqCBQvQaDRYrVYeeeQR7rnnnjqNYc2aNXz//feEhoby6KOPEh8fX6f9Xy25hy6EEMKtpKamsmDBAsrKyiguLqasrIz58+eTmppaZzGMGjWK7777jqSkJO6//34eeugh8vPz66z/v0MSuhBCCLdy4cIFNBpNhTKNRsOFCxfqLIbQ0NDyGHr27InBYODUqVN11v/fIQldCCGEW4mOjsZqtVYos1qtREdH11kMWVlZ5T8fO3aMtLQ0mjRpUmf9/x1yD10IIYRbiYqK4pFHHmH+/PkV7qHX1MS4l19+mY0bN5KTk8N9991HQEAAa9asYdKkSTz22GO0b9+eN998k6NHj6JSqdBoNCQmJhIaGloj/dcW2Q9dCCFElVy9H3ptzXKvz/7s85QrdCGEEG4pKipKEvlVkHvoQgghRAMgCV0IIYRoACShCyGEEA2AJHQhhBCiAZCELoQQQjQAMstdCCHENeehhx4iNTUVlUqFl5cXL7zwQqXHwex2Oy+//DI7d+5EURQmT57MyJEjXRTxX5OELoQQ4pozZ84cfH19Adi8eTP//ve/WblyZYU6SUlJpKSksHHjRgoKChg+fDg9evRw20fpZMhdCCGE20lOTuaFF17gzjvv5IUXXiA5OblG2/8tmQMUFxejKEqlOmvXrmXkyJGoVCqCgoIYMGAA69evr9E4apLbXaHPmTOHDRs2kJaWRlJSEi1btgSgX79+aLVadDodAFOmTKFXr16uDFUIIUQtSE5OZty4cZjNZhwOBykpKWzfvp3PPvuMmJiYGutn2rRp7Nq1C6fTyQcffFDpeEZGBpGRkeWvDQYDmZmZNdZ/TXO7hN6/f3/Gjx/PmDFjKh175513yhO8EMJ9mM1m1m/ayoW0LBo3CmfQzTeh1+tdHZaopz744IPyZA7gcDgwm8188MEHvPTSSzXWz+zZswH45ptvSExMZPHixTXWtiu43ZB7ly5dMBgMrg5DCHGFfvx5D0/NmM+PpyFf04pdp5xMmTGfn/f84urQRD117Nix8mT+G4fDwfHjx2ulv+HDh/Pzzz9X2u/cYDCQnp5e/jojI4OIiIhaiaEmuF1C/zNTpkxh2LBhzJgxA6PRWGUdo9FIampqhT/uPEQiRH2Wl5fHZ1/vJLzFjXj7BQLg4x9EWIsb+XTFdgoLC10coaiPWrdujUpVMT2pVCpatWpVI+2XlJSQkZFR/nrLli34+/sTEBBQod7gwYNZvnw5DoeDvLw8Nm/ezKBBg2okhtrgdkPul7NkyRIMBgMWi4XZs2cza9Ys5s6dW6neJ598wvz5810QoRDXnpVJGwmKia/yWEB0R1asWs+E8XfXcVSivps4cSLbt28vH3ZXqVTo9XomTpxYI+2bTCYef/xxTCYTKpUKf39/Fi5ciKIoFbZQTUhI4ODBgwwcOBCAhx9+uE73ZL9a9Sah/zYMr9VqGT16NP/85z+rrHfvvfcyYsSICmWZmZlV3pMXQlRPVq4RrX9slcd0em+ycs7UbUCiQYiJieGzzz7jgw8+4Pjx47Rq1YqJEyfW2IS4kJAQvvzyyyqP/fE+ulqtZubMmTXSZ12oFwm9tLQUu92Or68vTqeTtWvXXnY/WD8/P/z8/Oo4QiGuTTqNCovDjkqlrnTMbrfhq6sXv2KEG4qJianRCXDXAre7h/7yyy/Tu3dvMjMzue+++7jlllvIzc1l3LhxDBs2jFtvvZVz584xffp0V4cqxDVv6M29yEmteqJSbupxbh3Yu44jEuLa5XZfn59//nmef/75SuXffPONC6IRQvyZ1q3iaN94D8cyzhAY0RRFUXA6neSmn6ZTU29atGju6hCFuGa4XUIXQtS8H3/azeYdezFbnHjqVNzc53q6de1SI20/cP9Ydv34M9/t2IvZ6kSvURg9qCvdu11fI+0LIa6MJHQhGrhFH37OsQyFwIh4dIqC3elkyfpjHDl2mvvvHVUjffTs0Y2ePbrVSFtCiL/H7e6hCyFqzq/HjnMkxUKQoVn5WtWKohDSqAX7zxRz6tRpF0cohKgpktCFaMDWbNxJcFTVi3EER7VizcYddRyREKK2SEIXogGzWB2VVtz6jVrtganMVscRCeE+5s+fT1xcHCdPnqx07Nlnn6V3794kJCSQkJDAe++954IIr47cQxeiAQsO9Ca1zIxWV3mjlDJzKY2CZc0G4b6MRiMZGRkYDIYaX1/k6NGjHDhwgEaNGl22zuTJkxk7dmyN9lub5ApdiAbszoTB5F04WOWxggsHuf02912XWly7LBYLM2bMYPDgwUyePJnBgwczY8YMLBZLjbU/a9YsZsyYUSPtuQtJ6EI0YCEhIdxzazeyTu2itPjSRiklRQVkndrF2OE3EhgY6OIIhajslVdeYdOmTVgsFkpKSrBYLGzatIlXXnmlRtp/++23ue2224iKivrTeh999BHDhg3joYce4swZ91/GWIbc3cih/fvYt30DKpsFzxADg24fJcvYimrr1bM7nTu1Z/W6zWReTKNNTCi3PPAQnp6erg5NiEqMRiMbN26sdDVeVlbGxo0befLJJ6v1e3H//v0cOXKEKVOm/Gm9J554gtDQUFQqFd988w0TJ05k8+bNqNWVlzl2F3KF7ia2rl9D8oZPGRThYGC0huuUdD59/UWKiooq1LPZbPz8w042rv6GnJwcF0Ur6htvb2/uvjOBxx+awJ0jhkkyF24rIyMDjUZT5TGNRlPt7bD37NnDmTNn6N+/P/369SMzM5P777+f77//vkK98PDw8gmlw4cPp7S01O234paE7gZsNhtnfv6O65uElT8r7KXXMqS5P+u/XlZeLz0tlfdmTsG+91saZexh07sz+fbLJa4KWwghapzBYMBqtVZ5zGq1EhERUa32J0+ezPfff8+WLVvYsmULERERfPjhh9x4440V6mVlZZX/vHPnTlQqFeHh4dXqu7bJkLsbyMjIwKCzVyr31uswpWaUv1792fvc0Sak/FvjTX7e/HDqZ86f60Fsk6Z1Fq8QQtQWPz8/Bg4cyKZNmygrKysv1+l03HzzzbV6GzIhIYH333+f8PBwpk6dSm5uLoqi4OPjw3vvvYeHh3unTPeO7hrh7+9PgcVRqdzpdOLw0AJgMpnwNOehUkVWqNO1SSjfb9lA7P1V7w8vhBD1zb///W8ANm7ciEajwWq1cvPNN5eX16QtW7aU/7xq1arynz/++OMa76u2SUJ3A35+flj8G1FsMuHjqSsv/+nsRbolTARApVLhqOIOicVmR1PFM8ZCCFFfabVaZsyYwZNPPklmZiYREREyQfgKyD10N3HPA/9iZ6EXm09ksudcFknHcwnuOpTWbdsDl4abHAGRlJZVnPm5+mAyZRYbm9etxmQyuSJ0IYSoFX5+frRs2VKS+RWSK3Q3odfrue/xqRQVFVFUVER4eHilxyPumvQoSxbMJdCSia9GYfOhM7QM86erKpWSlLN8+soGet01iTbtO7joLIQQQriKJHQ3cOLYUb5fvRxVWQkOjSed+w0lMjKyUj1vb28mPzOd7Oxsjh09Qg/r1/RsYQDAx1NHQoAvK1d8Qut2c8tnywshhLg2SEJ3seTz5/j5i/cY2sqAolxatevHzctQqdV07Hx9le8JDQ1l8+lj9GoaVulYrKeNc+fO0bSpzHoXQohridxDd7Htq7+if1xEhSvqHk3D+OW7tX/6PrWHBrvDWanc4cTtH60QQghR8yShu1qZCXUV21uqrOY/fduNA2/hhzMXK5Q5nU6Sy7RER0fXaIhCCNGQlJWVMX36dAYOHMiwYcN44YUXKtWx2+3MnDmTAQMGcPPNN7N8+XIXRHp15FLOxTT+wZgtOei1vy916HA4cHr++azOyMhIIroNYf1PG2kTrKHEYufXQrht4r/k/rkQot4zGo2sW7eO5ORkYmJiGDp0KL6+vjXS9uuvv45Op2PDhg0oilLlMtpJSUmkpKSwceNGCgoKGD58OD169PjLDV1cSRK6iw2+YzRL3pjOkBaBeOt1mC1WNpy4yC0PTP3L9940+BZKe9/Eof2/EOLjwz/bdyxfRU4IIeqr3bt389RTT+F0OjGbzej1eubPn88bb7xB165dq9V2SUkJ33zzDdu3by+/+AkJCalUb+3atYwcORKVSkVQUBADBgxg/fr1TJw4sVr91yb57e9igYGBjHv6JfY5o9iUqfBjWQi3Pz6DqOjGV/R+Ly8vuvfsRfuO8ZLMhRD1XlFREU899RQmkwmz+dKtR7PZjMlk4qmnnqq0YdXVunDhAgEBAcyfP5/bb7+dcePGsXfv3kr1MjIyKjxtZDAY3H5zFrlCdwP+/v6M/MckV4chhBAut3btWpzOyhN+4dI8oXXr1nHXXXf97fbtdjsXLlygTZs2TJ06lYMHD/Lggw+yadMmfHx8/na77kAu6YQQQriN5OTk8ivz/2U2m0lOTq5W+waDAQ8PD2699VYAOnbsSGBgIOfOnatULz09vfx1RkZGtXd6q22S0IUQQriNmJgY9Pqq96fQ6/XExMRUq/2goCC6devGrl27ADh37hy5ubmV2h08eDDLly/H4XCQl5fH5s2bGTRoULX6rm2S0IUQQriNoUOHXvZJHUVRGDJkSLX7mDlzJosWLWLYsGE8+eSTJCYm4ufnx6RJkzh8+DBwaSvVqKgoBg4cyF133cXDDz/s9o8Eyz10F3I6naxeu5E9h05htUOQr467RwymcWP3/ksjhBC1xdfXlzfeeKPSLHdFUXjjjTdq5NG16OhoPvvss0rlixcvLv9ZrVYzc+bMavdVl9wuoc+ZM4cNGzaQlpZGUlISLVu2BC4Nizz77LMUFBQQEBDAnDlziI2NdW2w1eB0Opn79vtkWcPwC+uCB1Bst/Hau1/x8PghtG3TytUhCiGES3Tt2pW1a9eydu1aUlJSiImJYciQITX2HHpD5XZD7v3792fJkiU0atSoQvn06dMZPXo0GzZsYPTo0bz44osuirBmHDl6jAtGPX5BhvIytdqD8ObdWbpigwsjE0II1/P19eXuu+/m6aef5q677pJkfgXcLqF36dIFg8FQoSw3N5dff/21fFbirbfeyq+//kpeXp4rQqwRW3b8RJChRaVyRVEoMKkoLi52QVRCCCHqK7cbcq9KRkZGhf3B1Wo1YWe5otEAACAASURBVGFhZGRkEBQUVKGu0WjEaDRWKHPHxQCccPklWv+nvKioiN27dqDV6ujasxc6na72AxRCCFGv1IuEfjU++eQT5s+f7+owKikpKWHTlh0YjcX07H4d/Xp144OvfyEk6tIcAZzgvPQfAjwd5QscfL9lE6e2f8t1kT5Y7A4+2votfe6eROt27V14NkIIIdxNvUjoBoOBrKws7HY7arUau93OxYsXKw3NA9x7772MGDGiQllmZiZjxoypq3Ar2bB5G99+tx+f8FZo9WHs+XQr4T4mDD4q8vKzKLM4KS3MA7uV5OM/0LaRmj0/7aJV2w6c2v4tQ9r8fp5Rwf6s/OpjWrWdK5uw1DCj0UhGRgaNGzfG09PT1eEIIcRVqRcJPTg4mNatW7N69WoSEhJYvXo1rVu3rjTcDuDn54ef35/vVFaXMjIy+Oa7Qxia9ygvC23cluKiQmJ9LuLIPMqZQ4fx8fZGMWbwTDsduSUWjq9cxPKPFEZ1rLwyUaynjfPnz9OkSZMrjuP48eMkvv0+xuIy4po2YupTj7jV5+RKeXl5THpsGtlFatR6f2ymXFo08mHRO6+i0Wj+ugEhhHADbpfQX375ZTZu3EhOTg733XcfAQEBrFmzhhkzZvDss8/y7rvv4ufnx5w5c1wd6hVZmbSJ0JhOlcq9fP05ce4obb2Kuf+O9uTmZKMuLkOv1dDc6WTN8Rxa+fpxMSeHyGD/Cu+1OECr1V5xDC/P+X+s/+EMka36oA/z5kheOoNHPsz/e+kxuna9vtrnWJ85HA7uHP84IW1uo7Hn7+s45+VnMub+f/HlpwtcGJ0Qojakpqby8MMPl78uKiqiuLiY3bt3V6g3b948li5dSlhYGACdO3dm+vTpdRrr1XC7hP7888/z/PPPVypv1qxZvdhg/n8VlZbh4Vt18i2zq1BsFgBMJcWE/N+e6IqioFacDOgcx4I1P9KxZdPy4XWb3U6azYuE/3ms73JOnDjB+h/OENtpaHmZb1Ak3l3vZuqs+WxJ+viaHrr/cvlKPELaofWsuCmDT2AEKWl6zp49S9OmTV0UnRDXJrvdztq1a1m6dCkXL14kLCyM0aNHM3To0PLJ0dURFRXFqlWryl/Pnj0bu91eZd3hw4czdepfb2ftDtzusbWGplF4EKYSY5XHvLVOFJ9AnE4nWp0eq80GQEqOkQyjibScQlr1Gso3x/P55VwWP5zOJOlsKSMffPKK+3/97cVEtupdqVylUoNPY3788ae/d2INxOoN2wht3K7KY/4Rrfn623V1HJEQ1za73c4TTzxBYmIip06dorCwkFOnTpGYmMgTTzxx2cT7d1ksFpKSkrjjjjtqtF1XkIRey4YPG4QxbX+l7QALLp6nT7d2DBw5jlVHMvD0CyTNaOXznUfZcyaNHo282fTzAbw99TzwYiIdxkzhhvv/zT+ff5XQ0NDL9ud0Ojl58iR7f9lHcXExBcZStPqqtwTUegeRnJJSo+db3/j5emM1l1Z5zGYuJiw4oI4jEuLatnbtWvbv34/JZKpQbjKZ2L9/P+vW1eyX7C1bthAeHk7btm2rPL5mzRqGDRvGhAkT2L9/f432XdPcbsi9ofHx8eGpB+9m0cdfcSG7FBQNQX4aeneJ49ahNwNw15Oz+O7bFew6e5BBLaIJ9vdF6+nJ/Z0NnMw8xk87t3Nd9xv+8r750V+P85+lqynzCEWl8cSxYid2m5nC7Ato9F6knzmA03lpeF1RnDgtRnp0H/GnbTZ0Ux6fzD+eeofGHSrvolSceYS775J76ELUpaVLl1ZK5r8xmUwsWbKkfJGxmrBixYrLXp2PGjWKBx98EI1Gw65du3jooYdYu3YtgYGBNdZ/TZKEXgcyMi5SZgPvgEgcgEIhnp6/bw8YFBTEnfdOpPD8EeJbh5eXOwFfxca8t99Gv3ofKmcZ3eObM370yEr3vY1GI/M//paIlr3+cKwxWr8o1nz5Lr6R7Ylq3RdFuTQoY7OWkX7ga7y9vWv57N1b06ZN6dEmhN0ndmFo3h2VWo3NWkbar1sZN6K3LOIjRB27ePFitY5fjaysLPbs2UNiYmKVx/84GtqzZ08MBgOnTp2ia9euNRZDTZKEXkOysrLYtmYl1tJiGrfpyI19+6NSqTifnMLSpJ8Ib34jf3zIbtOeY4SG7KV71y7lZSqno0KbKRfSKC5TYccLVP4oCmzYlcKJ4y/zyksvVKj7ddJ6AqM7V0r0AaGNCA0Nwyu8GWUlhShqDQ6rCa3aweA7JrPsqzU8+s9/1PTHUa/Mmf0869Zv4v1Pv8ZiV+HjqfDO9PuJj493dWhCXHPCwsIoLCz80+M1ZeXKlfTp0+eyV9xZWVmEh1+6yDp27BhpaWlX9bhwXZOEXgPOnDzBts/n0b9FKPpQDeeObuTjQ/uY8K+pfP3tJkKadK70nuBGrVi/5afyhK4oCg6fUOwOB2qVCpvNhrHUQrbRgto7lBCfS1f0nipfNm/bgXP6HNrENWP4sEH4+vpyMbsQvW/jKuNT6wPo0a0zRUVFmEwmAgIM5QunZGbImvEAQwbfzJDBN7s6DCGueaNHjyYxMbHKYXdPT88aXSRs5cqVTJs2rULZpEmTeOyxx2jfvj1vvvkmR48eRaVSodFoSExM/NM5TK4mCb0GbFu1jKFtDOVXx03CAilIzuTk8WMYSyyovSp/zIqiUGyueEV+67hJfLXgNXo28kSvODiZWcy2FCvNu/QDICXlNGl5xTTqOJwLpQpKbgA/zV7M2OG98Pfzwmg2o9XpK/WlctpQq9QEBAQQEFBxkpdade0+siaEcD9Dhw5l06ZNlSbGeXp6Eh8fz5AhQ2qsrw0bKu9s+cc90evLeie/kVnuNUBVVlxpqDvSx4NVyz7HYjbicDiqfJ+ntuLHHxFhYPILr5MbcyMLf05j7ZFUTOYyzp4/zfkLFzidkUdYs26oPTxQ4UTv5UN4ixv4bOU2hgzoRd6FA5X6KC0qpHXTUIoKsisdKy7IpmObmGqc+bUnPz+fp56dwW33PMSI0f/ky69WXvb/r7tzOp1YrVZXhyFEBWq1mrfeeoupU6fSokUL/P39adGiBVOnTuWtt96qkefQGyq5Qq8BDq3X7z87naSeO8Pp1ExahESSUpBC0sEUmnboTUBQMN7/t+lKftZ5bunRoVJbGZlZfDj/bW6NVnFnjwBsDoW1xzax19SYRu2GYrFYcZYV4xf6+/ruvob2/Lz3APfc2p0vkr5HF9QCracXxdnnaBRgZ+ac6bz+9vvkZJYQGH4pgedfTCFIdZHhwx6q5U+n4fj111+Z9ORrhLceiF/LbjidThZ/e5CVa7aw5MO3Uanqx/fj3Nxc/vPZClKyirDjgY/WQc/r23DbLQOv6UWGhPtQq9XceuutNTqb/VogCb0GdB80gu+++ZC+LcPJzsrAaioi0wS9QgM4e/4gg32z2HfUiCm0HQ6dDwHedq5vbeDGHhVnSu7dd4DE+UuJ97XQMToKh8OO3W7nns6R7ExKRqXWYC/JQ8FGSNjvs+G9fQPIyDzGyNtvo2uXeHbu+omCQiPdRwwlKioKgGlPP8IPP+3mh92HAOjftz09e9wlv8CvwpMvvEHjLiNRqy/9s1EUhfAmnchO8eCzJcu4d9xoF0f41woLC5mRuJigpjcS2vz3f/47jqSSk/sF9987yoXRCSGqQxJ6DWjbsRPefk+wZe03HN59jk7RQYzs34Gvt/7IiI5RKIrC9YVlnMrfx6/JWew7cwHjVg82LXiOErsHj73yDn3738wX32zBUphNu2h/nE4nKkVB5eGBzeokSF2MszCF2LBgihX/CleDJYV5dGh+aRMXnU7HgH59KsWoKAo9e3SjZ49udfa5NCSZmZmUOv0JUVf+JxMS3ZYVa5LqRUL//ItVBMR2R+1R8Tz8Q6PYd3IPdxQUVJpnIYSoH+rHGGE9ENukKf2G302RJoQCq5bMPCOeKgeKomC2WNl0MJk95wopMHtgtdoZ1CqMKf3bMDQuiHemTOTrFcspxQ8/Hx9OZv9+T15BAZWaKB8n2qJT6PV6VP+zA1hx1hEGD7zJFad9zcjNzUWjq3rFPUVRsDvrx0hHcno+Gm3liZMAvmEt2bRlRx1HJISoKXKFXgOsVitvzPuQC3ngiB3CUVM+P/x4lKC8HHo09uf1HRfxbj4Yf79ArHY7gQEZbEr7kXaNoH8rA1lGE+9Mf4p2t7+If5PryP71FNtPZdE5KoBis5Vtp7NRtF7c08mbRT9tpHnPkcClK/PirCNMGj0Evb7qX9KiZrRs2RJrcVaVx8rMxUQEeVV5zN04ufwXDw+NDpOp6n0HhBDuT67Qa8CcN9/l1+NpWE0lBAaHkJ5XTOqJHyg0FvHk8gNcDOyB3cOHc/lWTubaQBeArtlAlh/KxeFw4KnxINRTzfnvl2A6uZX9ydnsTyti2f5M9mRayTKriY5uTLrdk38/M5meLVUEWo7To7nCGzMfo1PH9q7+CBo8jUZDt/ZR5GecrlDudDpJP7KBF5991EWRXZ0gP+1lZ+UXZp3hxhuu7e10hXvJzc3l5MmT5Obm1mi727ZtY8SIEQwbNoyxY8dy4cKFSnXsdjszZ85kwIAB3HzzzfVit0+5Qq+m9d+upGj/Jka1bUaZNZc1W3dQcv4kM/s3IdzPk5lbsygoOM2Jknx8o69D6+eJzW4n1ZRPZoGWT346g69OQ4CnjrHxAfhrjdgaNWZvhokQHz2rThjp0j+BiRPHEh4eLpPYXGjOy9N46tlZ/LL/KFr/aOyWUlTmDF6dOoGYmPrx+N/IhIG89eFqwptVnJBZZi4hxLOUpk1iXRKXEH909uxZ5syZw+HDh9FoNFitVtq3b8/UqVOrvZ1xYWEhU6dOZdmyZTRp0oRVq1YxY8YMPvzwwwr1kpKSSElJYePGjRQUFDB8+HB69OhRPtHYHUlCrwaz2cyRzV8zsH0TNDodPp46bmzsTYrdkxAfHV/9co6uBl8aRagpMWez4finFEQMIDCyJSYlmNNFCuEOM/sL8mhlCCDEV0+Ej5YyiwVHSi4tohszLiiAlkP6EBER4erTveapVCreSpxRvutTYGAgrVu3dnVYV6V5s6aMH9GTL1dtw6IJQ63zwlaUSVSwmn/9a5KrwxOCs2fP8o9//AOTyYTT6cRisQCwb98+/vGPf/Dxxx9XK6knJycTEhJSvoRrnz59eOaZZ8jLyyMo6PcFuteuXcvIkSNRqVQEBQUxYMAA1q9fz8SJE6t3grXoihN6UVERAL6+vrUWTH1z8uQJ2obqcf5hDfaM3Hyahniz4ddUWjcKReUTgYenH56+MCo4nMVbN1HqF4bKQ4vWQ0vTEB9ig7w5nF6A1VSCR4AXZXYnwSHBtGoeS5zTydZ9u4m/ToZC3YWnpyc33HCDq8P427pdfx1du3Tm2PETGI1GWjTvS3BwsKvDEgK4tDrbb8n8j5xOJyaTicTERBYuXPi322/SpAk5OTkcOnSIDh06kJSUBEBGRkaFhJ6RkUFkZGT5a4PBQGZm5t/uty78ZUI3mUw89dRTbN26FYB+/frxxhtvyCQsIDg4hDMqPVq7CfDGbrdjCPTjg13p6LFyd2gseocKnaUMcKJSe9CtWSQrjv+Al0aFITwSkzWDns3CuKF5OJ/sOkF881KcWk+G9bq0xntuUSlBYa2x2WwsWfY1m7fvpqSoBE+Ng8mTxtGn940u/QxE/aQoCm1at3J1GEJUkJuby+HDhysl8984nU4OHTpEbm7u3/4S6uvry1tvvcWrr75KWVkZvXv3xs/Pr0GsQPeXCf3jjz9my5Yt5a+3bNnCxx9/zIMPPlirgdUHjRo1IlsbSpgzC5OpmJKiQpb8eBIvNbQK9OD42VMUmqwE+XjSp2UkWo0HlORSWuhJi449KE3+idBwPZ4aNcE+epqF+hETHsT1nTthKSvDarOx/byR+8YM5qXX3uFkporIiHboG3tgs9t5a8Eyzp87w7333uvqj0IIIaotNzcXjUZTPsxeFY1GU62EDnDDDTeUj7Ll5OTw4Ycf0rhxxc2tDAYD6enpdOhwaUXP/71id0d/Oct9zZo1KIpCx44d6dixI06nkzVr1tRFbPXCvY9NJSWoLasOnObddbu4oWkwj/aMYmz35tx3QzPu7RZLqJean85m4O+lo9RqJ1prpLgwh6wLx2gR5oPJasdktdO1SQjvb97HvBWb+HzdDt76eisewVEcP3GSE+k2GocEotde+g7moVbTtl13vv56fb1dS1zUjq++XknP/iPo2nc4Dz7yFKWlpa4OSYgrEhwc/Jf7C1it1mrfIsrOvrS3hcPh4M0332TUqFF4eVV89HTw4MEsX74ch8NBXl4emzdvZtCgQdXqt7b9ZUJPT08nNDSUZcuWsXTpUsLCwkhLS6uL2OoFnU7HPRMfwsM/BJ1OT9sQDaF+XigqD8wWG0HeOrQeCs2Cvflw53GiIyK5PsyBZ9pmbonz493tJ8goKKGg1IKvzoNOEb7ENQplcNf23HNDW9oqmbz//sf4+IXhUcWQkNYnnKNHj7rgzIW7sdvt3NBvOHM+2kVwxzE06jqBE0WN6dznLnbskAVjhPsLDg6mffv2l32aR1EUOnToUO2E/v/+3/9jyJAhDBw4EI1Gw5QpU4BLW6cePnwYgISEBKKiohg4cCB33XUXDz/8MNHR0dXqt7b95ZB7aWkpLVu2RFEU1Go1BoOBQ4cO1UVs9caFC6kcvaiiUXgsOp0etVqNoqjAYcHuhEAvPSYbhISEEawxow9QkV9g5fb45gxtF8XHP56hbZmDiyVl3HFdE9YdzyA6PAq1WoW1pJhzx04T1b1TlX077A6XzGcwmUyYTCYCAwPlUTo3MXbCw2ij+hAe0ay8LCC8Cb4hE3hgSiLHdvd2YXRCXJmpU6dWmOX+G0VR8PT05Jlnnql2H7Nnz66y/I9bp6rVambOnFntvurSFc1yz8vL45tvvin/GSh//Zvhw4fXcGj1x3sffUXrG+8ia/tibHhgtVrQaHWgqFEpkF1iRqPWEual4KuUcTSvmJ5Nw/hi73km9GzBsA6N+HDXae7vFYdTpcZUZmXHL7+g12jw9gskzFdPRupp/GKb4aX7fdlXh8OBrSyXFi1a1Nq5OZ1OtqxbzfmDP6KyW7Hp/TGZywiwF+LloZDv0NGx3zC69uxVazGIK3PkdDbNew6tVK5We+Af1ZGPPvmU++4d74LIhLhyTZs25eOPPyYxMZFDhw6VP4feoUMHnnnmmWo/h96QXVFCv3DhAs8991yFsj++VhTlmk3ohw8d4sTe79Gxk9xCIxsOZXBLmxCigtSoVRpS8gspLnNgtNm4qYUOrCWE++pJKzShUSucvFiIXuNBq3B/AvQazl8sQKd2MKJzYxRFYffZbMo0wThK08ksCMLf2w8fnRqzxcqZE3t49onanZy4KWkl+nM/ckvTQADOnDzBzuOpDB/aB+3/bfCx/bvlBIeF0axFXK3GIv6corn88rN+IbFs27lbErqoF5o2bcrChQvJzc0tnwAnj1b+tStK6Jd7hOBad/78eZa98QL3tPTG18sXU6mWTQeO88nP52nsryW/1ML5PDNlDgjy8sBcnE/X2BAGtWnEsaxCTmQUsPV4JqO7NsVss2NTPDiclkFsiB+KomCzO/D11KILbUpcaBC39O3E5h17yc7OJSzYh+eeGEfykT18emAXUXEd6DNgUI0/epF88EeGtbyUzM1lZXgpNvo2D+GX4+fp0a45AL1ahLF5/bc0a/F0jfYtrpzdbsdUasRqtQGgUikV/i6YirJpFd/EVeEJ8bdIIr86f5nQP/3007qIo16xWq0sXfQOR3ZsoE8EgBZFcWIuNTK6WzPWH8uiqSGClIw07rsxFKNFxeHUXM6kpdOtaShOpxODnyebfk0nNtiH7CIzp7OL2XqmgEBDc348l0xEhB1FreNQkTfhrTujs1xg4ICbGDjg0q5qu7Z9R/qOr+jXLAy1SsWF09v5z5F9THxyWo3e01bbfn98pMxsRsFBqJ8nh87/vomHSqVCsZprrE9xdfLy8ug9eAwlxhJMxfl4+YXgcDpwWG1oNB44nU5yz+3hmc+/cnWo4hrlcDgqbPks/p6/urj+y4TetWvXv6pyzVnx6Qd09SogXeug2GQlKzuD8HAPtIoNb50HejWcT8/gzvhoFEXBWVxGu+hQvDBxPqeI2GBf9ibnEuKjw2J3sHjnSSINEdwz6AZ+OHqBkthBrEzPQaWAOvpGNNZS+t0YX96/w+Hg1x3rGBb3+3Kw0SH+GFNzOHRgPx3jO9fYuRrtKs6fPsmekymUmkxoVU5ySqy0bdu2vI7ZYkXtK9+iXSXh7klEXz+aGLUHBze/T9PrEvAPa4ITJ6UlhaQd2cCD4wY3iIUzRP3j7e1NWloa4eHhaDQamUT7NzmdTnJzc/90EvQVL/36v5Pg/kin09G6dWtiY2OvKsD6qiT9DPZwLWfTsmjVKoTror34+dwZUvNLiA3xw2iyEemvLf+Lq1ErUGaldWQAe5JzKSqzcSg1j+xiCz56FYFeGnwp44utv1DkbSAw2B+Vb3sURYWjJJVucSH07/v7pLOCggKC1Jee1TSbzWRk5WCzO/BUKfy4dVONJfS8vDxKCnL4Yt8xBrUMpWWrEHKLTBgtDo5kZlBiboJeq2H9iWzu/NfDNdKnuDp2u518s4YQb38A4gc+yPEfv+TC4U0oHh6U5mew+ot36dKli4sjFdeqqKgocnJySE5OxmazuTqcek2v1//p5jBXnNCfffbZv/xmNWLECGbPnt3wv4E5nWz6aT8P9W1FRk4+HiqFm1tFcDaniNnf7uGmzh0pLsrD4bx0NV1osmK3mLloKmXbiUy+P5WFxWbDZodR17fi7MVi1BoPwvzVhN1yBzcNHMKBg4ew2+3Ed7q10jcyHx8fimwKF7NzyMotRqP3wWI2kpmbT1JKAY3bbGbo4AHVPs3vvv2Ke7o2Y1VJNsH+3mSX2vDy9qVEZaVNIx/e33qY1vFdue2fzxISElLt/sTVKyoqQq31KX/tofWkXZ/fVw48vXuFJHPhUiqVirCwMMLCwlwdSoN31but/dkY/sqVK2nbti1jxoypVlCX069fP7RaLTqdDoApU6bQq1fdPy5l9QklSAulJhMBnlq8tR4oikLTUF/iIvw4dPYsZ/MtWMpKCfPR0iLMD7WHk+9T8pl0Y0ssdjvrjqRRWFJGkdmG2e4g2EdLh6hgduzdxaBbE+h6/eV/CWu1WgiNZe+RrYT7emIvyiLYW8/29GwGxzTho3cX0O36+GpPJjEXFeAVpkXnoSI0wO/3ckVDaHRTjG1iuGPMP6rVh6gePz8/7GWFlz3utJZUWZ6ZmYnJZCImJkbubQrRQFxxQl+0aBH/+te/GDduHLfccgsAq1ev5vPPP2f27NmcO3eOefPm8fXXX9daQgd45513aNmyZa21fyX6D7uDpFkbCNWoiQ32BsBsg/SCEs5kF4FKTZinByqVgo9eS9LhVBx2O5EBXmg8VMRF+BEd5MPMpAPsSS0iyteDA+cvYrZYSbOlsylpJQNuHX7ZkY6cnBzWf70cvcWIh9NKiLcOjVbH9e07ERQYQJiPjjcSE3llzpxqnWdwoybk5B5A5+VNYWkZ/l46nE4nilrDj2ez6Tt5crXaF9WnUqloHKqnKDcV3+CKQ3GFF8/ROjawQtnGzVt4fcHnlOGHWuuFo/QiN17XnJenT234I2tCNHBXnNAXL16MwWDgySefLC+Li4tj8+bNLFmyhCVLlvDdd99x9uzZWgnUndisFn49n06Xbk3QqFVYbE4yC4v4JSWXB3vHofFQEeil5VimkfwyJ2NvaMOXu0/Sr1UE209epGmIL746D+IbB7PP2oQjJw/x7sgOmGwO9BFNKTj3I9+tVTHgloQq+5/9xGQmxgcT6h+LqbQIpxN2nM7G01EEBGAIDuCHQ+erfZ79b7mNxXN+oXvLJmzdf5RQrQO1hweF3pE06zkAg8FQ7T5E9SV99TE9B9xOfnoM4c26Ak6yzuzG05rGsg3Ly+vt/P4HZr39FTHxd1ZI3vvTTvDolBeY/8bLLoheCFFTrjih/7ZiT1ZWFuHh4cClBe6zs7NJT08HLu1Oc/r06dqJ9P9MmTIFp9PJddddx5NPPomfn1+F40ajEaPRWKGspvewXfTSM/hq1Ww9kUnvluHghN3ncmgfFYTFbifQS4tGpdA63I/VRzPw8PCgY3QIxzKK0HioUClgA/z1GkryC4mKbEFxmQ2zQ4VeUdHIR8u2Qz9DFQk9JycHA4U0Domg1OIAJ4T46Gjkr6fQWIg+qBF2mxV/v+rvW6/T6Zj87Cy2bVxHiBJOqV0hrkNnhnW5Hm9v72q3L2qGWq3mp62r2L59O6+//QFqtYoF0/9J9+7dK9R77e2PiImvPPITFBnHwQOnKCgoICAgoC5DF0LUoCtO6AaDgZSUFIYMGUJ8fDyKorB//35KS0uJiYkBIC0trVYnRy1ZsgSDwYDFYmH27NnMmjWLuXPnVqjzySefMH/+/FqLoaioCGN6Mvd3a4zDATtPXySrsJTzeSUMaB2JSlHQa9RY7E7UKgVvrQpTmRmVWk1BsQMV4HA6KSi1YLI50PmGYtcqaLR2PFVgzEzB6aXn1IEMDu3fS4f4ivfSUy+k0CTIC61WS5mllDK7EycQE+TFwYxSwp2QnJFBryFja+R8tVot3Xr15ev//IpHQQZntpzj5J6dDP/HP2UinJvp06cPffr0uexxY5kHgcr/Z+++w+OozsWPf2e299Vqd6VV71ZxL7hgTDNgG4NNh0CAEAIBEsK9gYQkN4GUXxLS7g1JKAkECARCM80YbIwx7jbutmTZKlbvWml7n/n9ocRGsQmycYX5PI8fnh3N7LwzSPvuOfOecw7/vNziGc2LL73K7bd99XiFp1AojrMRJ/T77ruPgCNTvgAAIABJREFUb33rW4TDYdatWwcMFcipVCruvfdempqa6OjoYPbsz15d/Un+1cWr1Wr50pe+xB133HHIPjfddBOXXXbZsG1dXV3H7Ll+JBIhy6rFbtChEofuwcR8JxUeO8mUjChCdyCKx2ogmkwRiktoVSLbWrw0DUS5caKbDY297O8L0h8TceWPprl6Jf6gBkFUk+V2giCQm25h06KnKSwZhcVysLWdl1/AMn+KyTIYDXriySQratqp7hwAVOz1rqNkxgXMnnfJMbleWZZ59v9+ziXFJnRZQ+PeE8kULzz8C+568NdIksSG1R/S193B6ElTKSk9ufUNik/2n56Qi4JIIpk6YbEoFIpjb8QJffbs2SxatIgnn3zyQLd6WVkZt9xyy4EitU2bNh2fKBla9S2VSmGxWJBlmSVLllBRUXHIflar9ZBu+GPJ5/ORZtCQkmVW1nYzMT+dwnQz0USK17a3cvWkArr8EXqDUXZ3+PCGE/x2eS3b2gPY07NZXd+Dw6ijyRuiNmzFqK7HI3Xw9w0pLptYwPp9rXTH1Sw8dzoysGLJmyy45uCXEYfDgWP8OWxtWsdoj5UPatvJtem4ZGwuVpORXV1BegO+Y1bgVFtTTZkxjk5jO7BNo1YxzgHL3l5M/aYVnJGpZazFyJ5FW1il83Dz3fcpldOnIKM6MVTUeJjfDX9nNVc9+IOTEJVCoThWjmjY2qhRo/jVr351vGL5j/r7+/nmN79JKpVCkiSKi4t54IEHTngcWVlZ1PeFcLd62dE2wITcdLyhODajlikFbt6pbqc/FKPdn2C3z4QuezKW8WcRTyyiv3MDH4UkJuW5ELUm8iquILZ3MXeemYs/muC92k5yXGlUVpTS07qfWGCA1za9yoevPMWsq77KwquvA+Crd9/HO6+/ymOLnscajlNQ6sHtsCMIAplOB89u2k5PT88xGffZ09WBy6w9ZLvbYuTZt17grnOrDiTvSQVuOgYGWfbW68xZcPlnPrfik/X19dHf309mZiY2m+3TDwDu+uqV/ObJZeSOuWDYdl9fK0UZ6gO1MQqF4vR0RAm9traWRx55hN27dwMwZswY7rjjDsrLy49LcB+Xm5v7H2erOxFkWebFZ56g0+tj0G2k2GXBZdETS6Zo6g/istvISreyqbGLrmiMBy6fRmt/gLcb36Fo0mU0rupmZlGEBRMKeWpjMzFBxl4wHkluwGXRc9vMUbxT28Wumj3MHeWkLxLn+mlllGam8dyix8guLGbKlDMQBIF5l11JY2MDY70CGenDP9DHZZhZs3o1l19xxWe+5qpxE3l/3WI8juHn+Gh/L2Uu8yEt8aw0CzvrdgFKQj8eent7efjPz+ONaBE0ZuT4ILnpGu7++o0YjZ+82hrAJRfPwTswyFMvvoxozkHUGIgNNlNVkMYjf/jNfzxWoVCc+kbcL1pTU8N1113He++9R0dHBx0dHSxbtoxrr72WPXv2HM8YTxlL31hE0/IXmZCXTkWmHaNWRV2PH0EQcFv0NPf5ae4P8cHebtwOO9FEgjynlWsrjSS6tuMunEhPMEZnMElnysFAbzMpk4f2QIp4UkKlFkGSMJCiPxSjsT9MScZQ1fFVE/N56bHfDYunpKKK1oHwIXG2+qOUjTo2S5k6nU7E3NHUdngPbKvvGiBkz8Ng+IQEoqzOd1zEYjF+9tsnCevLCcgOBqJagmTQncrh//32sRGtinjTDdey4o0n+M19C/nRrWfy7gu/5bGHf6E8IlEoPgdG/Ff88MMPE4lEsFgsXHDBBVxwwQVYLBai0Sh/+MMfjmeMp4yOPVuwqVPkpZlQqwTOKnFT3TnIzlYvCCK+UJgV1U1cMzGb87JlNny0hvr2DuxGHbZkD0Z7Nh91JvnDhn58aZNIqi34fX04jSIgkBI1NPribO/wMxATuGxK6YFlVGUZiB1M3olEAqNex9t7uunzH5wNbCAYYW9Yz+jRo4/ZdV9509cwnLGApV0iSztBHjuH27/9PbyCaVgSkWWZmvr91NY38Lc//Za6vbXHLAYFvLN0BV7JTWu3H0ltRdBaSanMtPWEaOgR2LW7ekTvI4oiEydOZNasWcrwQ4Xic2TEXe7btm3DbDazZMmSA8OVent7mTt3Llu2bDluAZ5KErEo6SYtbd4g5Zk2VKLIvNE5NHtDvLK1hZa+Qb5xTjkOkwGtWuA2h5k/flhNfmYGUVnFoLcDIf9cwq5SfN11ePJGE659GU+xmb5gjO1N3ZTNupj9u7YyscBFIiXx3q4mpFQKWZaIRI1s3bgeZ0Ymbz35f5yRqec7V1/AU2+tIJFIYLOnIdmz+PkT/zim1y0IAlPPPIupZw6fZnfe9V/jtSf+lxk5Jpw2E0vXbKJtIMw35p+NKERY//IjtE+ZwzkXzTum8XxRfbS9hqicg854cO52BAGd0UpUSvLOspV0dffiDwSZNGEshQX5Jy9YhUJxwo04oYdCIfLz84eNPXa5XLjdblpbW49LcKcSSZLYVVPLdYVW1td30x+MolUZMWhFDBo1Nr2IVqXiH5ubUIsCBekWLqj0MCXfweJNu0h4rqT7o0WMnbaAUFKF0Z5Ff2cdhsFWFm0zYtJp6YtKlOZIXH/P//CnX95HujrJ3EoPalGgM5Tg8glnsGbp87zdH+XW6QUkkklkWeb+my6jazBAp2si8y6/6oTdk7yCQr76w1+z+v1lLN24lkxzGredNfVAFfWZpZm8vXE5M8+/ELX6iJcNUPybuvomNPmHr1dJSSIvvr6UXikfrd7Aym3v4TKG+c63vobBYDjBkSoUipNhxJ+yWVlZNDY28vTTTw+by72xsZG8vLzjFuCpYsum9YzLSeOBN1eQk2binep2pha6UKtENjT2kmM38j8Xj0MQYFuLly5fmLd3t2HRaXi/uhXt4CpEjYFAdx12zyj8pNG8fQk/mZaJLEOm3YRGp2dzbwdTpk3H8LPHWPT/7mYgKaI3WamaWIxKFJmSa2fLtpXUuiAcl2ntHUAtykwalUd3/S5gZAl9z+5drH37ZcSID0mtp3jyLM6dc/ER3xedTsfseZfQ3bqf82yWQ4ZEZelSdHR0fCF+R443T4aDxr4WbO6CYduTySRdLTW4csqxOlwA6PNHE4uG+d8/PcX3773zJESrUChOtBEn9Llz5/L444/z0EMP8dDHFv0QBIF58z7/Xao7Nm+iramBMreVO88ZRUt/iFhKpm0gRLHLQmWmDQEQEJiQ6+CRxm5kWWB/fy/mjHJ0ZisRrYGe/m6KiqsISyJ2Vx4f+CEzupfxBS72DSQw/XOFNFEUmTVpDOW5w4cSRSMRpFiM2o4AvoFuRnssJFIyr3+4GZ/BNaJraW9v46OXH2d+ZRYwtDRrzZ4P+FCl4uwL5hzV/bE53fgGe7Gbh7cGB2LyiIdVKf6zmdMnUf3CSrRGKwaz48D2vs5GpKgPT/HweRl0eiPtHUND3JRZ/RSKz78RF8XdeeedTJ8+HVmWh/2bMWMGd975+W8BBL29hEJhphY6MWjUFDjNmHQaCl1Wzi7LpKbLR0qW8UdjvLSlidHZDs4t95DlcmFXRSkrriTq70YvpGju7MTXtRdRrUP0TKZDdiOKKrZ1RbjoyqEpW7Ozs2k/tICdnv5B9gdACvdzydgcCl02yjLtXDEhH393B/X19fzjyUd57rGHqd6547CVzysXv8a5ZcO/KFRmOajfvOqo7885F81n5f7BYefzh6PEbNlKQj9G5s+9gLICN5pYFz31a+iuW0dv/RqkSD8Gg4b8kjGHHKOxZFBX33ASolUoFCfaiFvoOp2Op556io0bN7Jr1y4AKisr2bp1K3/5y1+46667jluQp4I0i4m4xUBCGkpYiZSEJMuEE0PfilIpCYAVtT0sGJeHNxwnEJeYVVVCUVDghepVmM02hFQYX9NGvDEV6VllxJMSXm0Bj+5o5u4Hf0vVuPEA2Gw2hMxSOgc78NiHiqBkWebt3R2krHmUZchIkowoCsQTSbrDKfIcFt7+7Xe5bOY4NCYVO5c+zda1Bdzw9buHdYWnokE0dtUh1ygkYkd9f8xmMxd95W4Wv/w3dJEBEqjQeYq5/o6vH/V7KoYzGAzcc9tVPPr0q2R4RmO0pBP29bLzo/eZNGshKtWhf87JaBBHWtph3k2hUHzeHHGl0tSpU5k6dSoA8XicW265BUEQPtcJPRqNsn3bNs50Glle005huhmHSUc8mUIliqxt6MGgUSMhIIoQjCWQZJlgSotaI5FvVeOyGGjoC5GWkU24YQW69DNId+fjj4YpGDuN+26+j1H/Nnb82lvv5M2X/s6WfTsRU0kko41AxkTclhBYkjT5+hCQEDQGBJMVA53MLHai/WcB2rg8Fztbm9i1Yztjx0848L6O7EK8g7txWA6OI5dlGUwj/+D3+/00NzeTkZFxYEa6wuJSvnb/T4nH46hUKlSqQ780KD6bstJifvez+9iyZRvNbZ0UF46hfrSNTY2H9sTIsowu1UdZWelJiFShUJxoSunxCCx++QW+MqOU7z+6iYnZNlbUdjGr1E2aUUtjX4hXtjZhNVnY3j6IJEuUZViJJiHTqkOvSRGMRZH8/cRiBgYGB4lJOuwGDYH+JnIzs2mvXU9BwZcPOa8oiiy8dvj2yGNPs2zNPpp628lJzwBhqAK/uqGFUW4Tac7h072OyXHy/oYPhyX02fMX8sRDWznbI+GymQlH4yzb10vpOZeydfMmqsaMQ6fTHfZeyLLMK8/8hVhLNW5VjJVtPTRH1dz6nQcpr6gEhhbPURw/giAwefJEJv9zIb4xo6uo/tWf8PUlsDmzAIjHonibN3PXTfOP2bz+CoXi1KYk9BHwtzfwXt0uRrktSDJk2/U8s6GBTLsVpDh6tQoBid1dIewGHbGkRFG6GVEESaVlX0s/55W66K6JYC+eTnskQKY2Qo7Gy662EAWqZlqamygtO/zsbv+qVxBFEY8rDe+gl7cGE1yjD+MwGxEEkZ6BQRwaLcZ/G6KUkiRUas2wbTs2b0QlCvx9TQ2JZAJ7bgk6WYYd79KXTPDgnzoomXIOX77164ck5/cWv4GzZydiogeCYc7N1BJOJHjqh99g4ryrueYWpYv9eGhoaGTR4vfxBeNo1XDumROZeeY0BEFApVLxw+9+g+UrPmTjtm0kU5CVbuKe+25WiuEUii8QJaGPQCgSZW99I7dML6Q/GGXxrlYmFWTQG0qiQ+YrZ5bR7ovgNOtYW9/DK9vamT3Kjc2gpqa7k6SgpSLfRK4tiV+G7JIpxBteIafQRlNXNWefM4Wdm9YdSOiyLLNj6xbqa3fTVFtNmhBDEGQkawa1A2rOnnMVu7es4vEte7DTCgKYCs7C37eZi5LJYWO+N+7vZcb1B9dG3755E12rX+eyMheUnQHAs0tWMn1cJd3eAfq6u1iYb6ej7n0ef6CW2dffQcXog8VWTTs3MTrRRyoWxGMd+vKgUwmkqyIYOmuordlNeeWxm6VOAavXbuSFJR/hLpiA1jz0GOOVlY3U7Gvk9luGVuITRZELZ5/LhbPPPZmhKhSKk+hTE/r555//iT8bydzRnwf+BDiMaiw6NXaDhT0dPiS1Ce/gAJPyHCRTElUeG019QaLJFP5wDI/DRiQep8htx+QqJuhtA40RrZCgMMsJkVzmVpkZ7bHQONBLmjjUig4EAjzzu59RZU7QuLOas4vsyGo9uUWlpCSJVatWU1xwNpNnzgXmDouzbpPIK9taOLPQgV6tZntXEPf4WeQXFB7YZ9vKd5lbdHB4WywW4/xSJ+/vrMWpE7iocqjL1mHSUqLXsPKlJymr+O2B5+G97c1oHFEMWjWiAP9aZVsvyBSnG9iy+gMloR9DqVSKV5esIbPozGHb0z1F7Nq/k5aWVvLyck9SdAqF4lTyqcPW2tvbP/FfR0fHiYjxpDNZLOhUIo19QRCgO2lk6/4uNKLAmBwHbqsBo1bFrFI33zxnFHaDiuY+P3npVlSiiCxLdIYF2iMa0i0GVKKAWqMFUU1Skil3GujvbAHg1aceZX6RkTSjllyrhgyrCZdeoKutBa1azVm5Bvra6w8bp92Zxld++FtilRfRnTeDS+/5KRctuHLYPkLy0Ep2QRDoHxhkRvGh49jLbQI11bsPvO7yRUBKIckyMkNf6iRZIhRL4PcNolGenx9TtbV7SekOv6ypI6eCJctWntiAFArFKetTW+hTpkw5EXGcktasXM7ejavo2LKabm8QtTiUwAS1Hq0cRSUKWPUaBEEgwzLU/WzVaynPtNESSLFzdQ0ZTjdNjXtpIJ+urgaqKiaRTAi4hDB9QRWotBRnprFswwdEIv9Fsr8dtdPJ1roWHKIMyKhEkVRkaFB6aY6bFXW7ceUMr1yOBP2U5dix2WzMPPeTe1VEi4NEMoxGPdTi1ul0DESStPYH8AaCuO1DX0IGokmystLxdg8M64lRmW08v2kTF1ZmYTdoiSdTLN/bSYHTyqZmL1d9+ZJj/H/hiy0WiyGqDv8lSaVSE48nT3BECoXiVPWpCf3ZZ589EXGcct55/WW0jRu4IMeG3KBDChip6RjEoFGRzgAem56UJKMSQK06WEUsyTIatZozCrL509JOloVcxGNhiiZOwRpIsH3HOgqsMCNHRVxjRZ30s7+jD6OQZNV779LZ1cWrLXsodujZ0dqLy6gm3W5BkmQ6u3pYV99JXqabrrp1mDPK0Wh1+Lob8FgT3H7H1z71uuZefSMv//6nXFiShtmgo76xicU7W7hgdC7b93czNidBUqXFnVOAWq1mz6DMrVVDXeiyLFPutrB5n8A71R28uaOFNKOOAqeJdU19XHTHnWRlZR23/ydfRBUV5UivfggcOnWut7ORSy6ecOhBCoXiC0lZBPkwEokEHTvWUZWdjkalIpwSMBt0IMCr25q5+5xRpJt0JBJJNuzvJ56SSMkyKUmm3RfBoNOSTKaw6gVElQqd0UJb9QfkVp1HTFIR6GtGbzCg1ehRCRKr67owqSQa9+6mq7mBSW4NdnWKNKOauu5B9nf10+lP8v6eHvbrx5O0jkVOhCky91Dl9HHfV+fwg3vvGtFwMZfLxQ3f+Rk71IUsqo/wTnUb37jiIi48eyZB9Kzc10WfL8hAOMEbuzuZvuD6YUV2zU1NFLntFDktWPRa6vuC1PbF0ReMYf4JXBjmi8JgMHDG6FwGe4cvgBQNB7EJPUyYMO4kRaZQKE41SpX7YfT09JChG5r5TRAESgvyeKG2idFZaUgyFLksfFjfQyQls6q+h9bBKJPzHAxE4iRlgXJPOmsb2gkkRGw5JYiiCr3VSV/bbkyObHoCWn71QTWj3Z2kaWFsTgYeq4b/e/EFJmc7eKc+Qold4JwSNx/s6+J/ayWcRcW4K2aSm1kAgDXdQ039Kn73sxuOeCUzq9XKFTfczEtP/4X5BRqkVIpn31iKQyeQl5fOxv09PL+pievu+SHjJh185NLYUE+WPsUlE0uJJ1IMBkPEkxJv1fZxyZe+eszuv2K4G669HMvipazZvJ5wXEAjpCgrcPG179yljDFXKBQHKAn9MOx2OwPxg69HF+UwEE3Q3J/EqtMgCyKZNhNrGr3ceVYea/Z7eXpDA1qdnrg6jed3txD1dWKdcANhXxdGuweLI4dAbxMmsxW9FCQkFRFI9nDJhFxEKU5PYJCg6CJUcAkGvZ6Nvn5Wbt6EMaUle8ICnCVjsFitB2ISBAGNo4wVK1cf9VClVDJJW/t+6ptaKLapqcq2IwoieelFzEsKPPvKkyy8+toDXxjWL1/CvDMn0tnSSJpehctuJRCNYzGbmTRtxme654pPJggCCy+Zw8JL5pBMJlGpVEoiVygUh1C63A/DZDIhOwsYDEUBeH3VFgqtOvpDMep6fOxo83HxhBISugw2twwwPstCWkYJ2VOuZ+zZX2baBTfjmHgDvmCYeDSI2ZGNLMtYnXlE/f0gS5QWV9ExGEYQVaRSKTb3CpRPnEMoCaIANns67so57A6YCMZSmC2WQ+K0pXuorWs66uv0+33sbemgzx9iQq4DnUoFskx/KIbDaqLUKrJly5aDB8gSRr2e/LJKksZ0BmQdxow8CopKkCTpqONQjEwoFGJwcPALM1xUoVAcGaWF/gmuvfUuXnzyEeS2/eyo209Ll5dyjx2NSuDxzf1cHEihMafRFe6msTFJ1axLUKs1pCQZQRApzs1DDKrYt2kRoiASC3rRmRy09qxg8vipBAKDqFNB4ilYvrORXvcs8vQGJEmmNxDDbtQOLf4imAlLwmFbZNFwEIfr6FcyU4f70NqcNDe0k5JkZGQGwnES0tBEJYIgoNEcnGVu7PRz2LHsGcbnu3G6hoa4SZJESKfGZDIddRyK/6y/v58/PfECPT4JVDq0hDhzUjmXL5irtNQVCsUBSkL/BFqtli/fcQ/RaJQ12y5ltJggN83M9pZ+ei1VvG+ajTf4D/LdDmKSmaXvv0FEUqHRmZATYcoKy1A5KrCk56PRmxEEkc5968goGE9E66KvayujnWoeXLyDSEJmWr6N/lCcAoeJlCwzGI4hyzJpjgwC/R10d7TjzsxCEA9+gPs6qrn0pq8c/UVKMudPqiIcDLGxuY/yTDsOiwFvJIksS+wdTHHnhINV1GPGjWfv9o9YU7ebsdl2vMEom3sTXH77tz/LrVb8B7FYjJ/85i/YC2bidh78c11b20IqtZirr1CGCSoUiiFKQv8Uer0evcWGPdWHqBLRaLRIySTR4ABj0hLs643jk6Fw4gLSssoQEEgm43TVrcO/6wPMjmwS8TByMkEqEcVdMI6B1l04HU58/gzS7AJWxygCKR2B5j1oVJVoRAFRgJQkk5nuIN60kv69avq7crCkOcnMzsXbXsPcWVVYP/Zc/UipHR6i8RDnTq7ib68vJZFMUZ5hoyuc4oVdfVx338+HtQAFQeCqm2+jo6ODHRvXkVbq5uvTZhxxUZ5i5N5+9310zjGo/u0e2115rNu2nssXJJX7r1AoAOUZ+oiMnjSVSDxJLCkTMWQz9uzr8e95myuqbMRFEwXj52FzFRAN9pOMR1CrtWSXz0JrtBLyddO1bz1ttatRaw107PmQQPt2CgtGga2IZHQQu7sEq6uAtIIphP3dSKIau8mAJ81CrHsXP74onyuddcT2vIGpdTn1K//MPTfNZv7c2Z/pui678Tae+aiZFR+uZv6YbAYjSf6wch8vbN7PtCu+yvSZZx32uKysLOZediXTZs5SkslxVre/HbM9/bA/S4o2Ojs7T3BECoXiVKUk9BGYf9WXiCZTyFIStc6IXqvBmT+Jde0SklpPJOilp3kbYX8fva07advzIclEjKzyWQT6WnAVTMRTcgbhwQ50Oj3udDcatZqkqCdpLURrtBAO9KHSmekeDBKNxenzB9m6+QMyk61oVCom5zu4sEjDd+eWcOVYBzbb0bfM/8VqtdLf24vHYaW2L8LUUfn84rpZ/OrqM/nozWeV4qsTLB6P4/V6SSYPzv6mUas+seBQlmIYjcbD/kyhUHzxKM2rEXj5mScIJ2WQwSr7SUQDeCrOYtPybUgRCVOkFcHkxpg+Fo3OSCoZo33Ph3jKZmKwpKPRGYlHA5ideQz2tTF2/BgisQSNrfvJnnwdyVgAuyUNt9tO524fps6V5JhS3DrJiE7M5t0djcyfWEJCHipUs2oFfD4fbrf704P/FFY5xJi8gmHbBEEgxwhdXV14PJ7PfA7FfxaNRnn0ib/T2OFHVhlQyxEqi9xUlBWi18rsadhObunEYcfIsoxNGyM9/fCtd4VC8cWjtNA/xc5tW9m5/HXKcvOo7fFz+yQr7TsWI0sS2lgf15aLfL0swMXGXbDxlwR6G1CpdaTnjqFz3zpMjpyhrvbaNSSjYVz5Y6nv6GJ/234SahuCKKDW6oknk0ixCCVpAmPdElYC7OkcRC2KaASZlbUdlBYMTf/ZHBIpKCg4JteXQnXY7ZGkpLT+TgBZlvnJr/5En1CAq3g67oLxCGmVvLVpgJ88/DJNIQ9N9bvZtGY5UmqopR4NB+mpW83tN11+coNXKBSnFKWF/imWL3qeAoeF8UW5vLZ+OzWdfr5emuDhJT/hpomZnFORywe1rVR3xXHbLHTsegJfxjSsFZewt/UFskpn0FW/AaM5DY0oo1GJxLqrKU8T2J/QgSxjsKQT8XXh7e3CHu1g4ehCZMmN1xfk9e1NmPU6crLcVBZms6K2k1FnXTJsONnhdHR0sOqdN0gmYkyeNXvYkqbt7e2sfW8JsiQhpeXQ7QuRYTs47CwcTzCgTsNmO/ohcYqR2bZtByExk3T90P2Px+O0dfZjyygmFvYhCCJnzb2R5n07qF7zPOPGjqEs084VN9+B5TBzEygUii+u0yqh79+/n/vvv5/BwUHsdjsPPfTQMWupfhIzcRoDUeq7+phekoFVryEYiZNvjDO92MXiXZ1sDWeRP2kGOp0Bnc9P894NtH70AslYBGfeWORUgp59qyjJzicZaubysU42tgZJyx5HLOJHb7SSTETo3PUaP5qfQziepMsXxmUxcs2MKh7b1I4nfxIbE3rO+dqtn7oASvWO7Wxe9ATnlrnRmFRsfvMvtNSfzYWXXs7SN15hcMeHTC92ISBQG/Py4Os7qXCZSTfrcTms1AQ13P/7p4/rfVUMWbtpO47MUQded3b1ojUMfZGyuovoaKmntGoy+WXj6FNHuPcbN6HX609WuAqF4hR2WiX0Bx54gC996UssWLCAN954gx/96Ef87W9/O67nFA0mggmobWlnfmUWT6/dy7fOq+CDfd3IssDqfgsTph9crtRgMFEy8WK6VryEqNGi0ujpaNiIWohjsaQR6t5EAyJN5JKmldDvfQavZERwl/GlyZm8s7sFi0oiz2FiU2cvvpiEy1PAjXd8a8Qxr393EfMrDyb9KYUZvL1tNa/H4ux580nOH5VBV3OAnV1BpL52Hlo4AdQ/b+tyAAAgAElEQVRa+vwhXtzWwe0/f5TMzMxjeh8Vh6fXaUnG4mg0OgASyRSCWkRKScRjEWzGgwvuyGoLAwMDSl2DQqE4rNPmGXp/fz81NTXMnz8fgPnz51NTU4PX6z2u5y2ePAu7AWraurEZNHz1zFL++GEt1T0JFu3sxpb3sWIlWSYlgyCK2HNGEw/7adm1jIC3DUmbzvp1i2kNa9mYrKSkuAqz5OWqqeX81/mlGHq20thQT3GajmlFborcNmaWejij0E3kCJe8VsXDh2yTfd1se/NvXFyRgdukxaWHjdt3c+O0Qsw6NTqVQJHHxT2zK3nu4V9+xrumGKmLLzoHb1vNgdexaJRAIEQoEqdzfzX+hI6BgcGhHyaD2O32kxSpQqE41Z02LfTOzk4yMjJQqYaKuFQqFW63m87OThwOx4H9/H4/fr9/2LFdXV1HfV6tzkCGUU2fQUV/MEKew8zYbAfbAiJv10codWqJJVKoRIFYUkJW6ZFSKSS1iWQiSnfDR6h1RjKKp+AsmkJosJN0g0QqlcAW7yDNVkgsNEiJFZr7Biiya+gaDBBOCpw/phCrWY0zYT6imCW9FVmWh00KU9fazfiiLJKpFCDTORDAadIgSRKiSgUM7WvQatDE/Id/Y8Uxl5WVxeRR6WzfX4dodJGUVcRjQQL9bdjTM9FZ3LR1DyKn4hRkmjAYDCc7ZIVCcYo6bRL6SD3zzDP88Y9/PGbvt3HNSryBILPKMtje6kWSZDQqgYG4CknjpNOfICZEcZt1mAwGookkPcEEPR11TJpzJypRRSg4wOC+FYQNZiwWBz3BbjoHGnnggkKaG3fQPBClzx/kxqklFKWbEAVIpCRe2VzHFedOI108suKnC676Mq898Ttm5VlIxKMs2bgbfyCAxwBrG7qZ6DFj0oiEYkmQZaKxBDrT0HPZREoiLvzngjvFsfWVL1/Nxk2beeChxxGMWQQGu0nJKqzZM0gl44QGOqlt2Mkvn3/0ZIeqUChOYadNQvd4PHR3d5NKpVCphlYo6+npOeR54k033cRll102bFtXVxfXX3/9EZ2vrb2DJ599lVUfbOPiNB2+SIIrJuSxo32QnZ1hBJUDqzOfgbqVuM64jP6oQFc4BmoDAb+XyEArSV8ug0EfxYlavjE1jXRtBFlqZmNjN6VlDh5fWc11UwrJyDFR5dYjyzIyAmq1CkmUmVqaTYsvjrmg6Ihizyso5ObvP8Qrzz3Nng2LuX7WZFZs2k6BTc3i3gFi4RBnFrtRiyJLa9o5v9xDNBzEYDLz1s52xp6z8IjOp/jspp4xmfKKNTgKpwEQj0VpqttFzNtGSWEh+ly1UgynUCj+o9Mmoaenp1NRUcHixYtZsGABixcvpqKiYlh3OwzNfvZZ5jcHGBgY4BcPP4erZCajzkojsPV/KUw3sbXVS6XHzur9AbQaNQXRXVw2M4fVze/gV2eSEC20dHcTi0YYP3k2bY27kX37WXCmi/FuFTq1FpBxmrTsaBvApBUocllY39jDqEw7bquRdl8Iu0GHxWxCIyZ5q36AO64685Au9E9jNBqRIwHunn8mgiAwvryUZTW1ZJq0nFOexY62AXLSzTQNxnhibT1mrZrOUJKcyedxz+13fab7pzg66o9NCaDV6SkbPeXA63hXz0mISKFQnE5Om6I4gAcffJDnnnuOiy66iOeee44f//jHx+U8Ly56m7T8MxBFkYzsYrYPqCn32FGJIr9fUYNdm8Amxrj+jAI8Fg1XV9m4uijKZZndlBsGicdC7Nm7m5g2jYKiMVj1IrqPfVo7TDpC8SRGrQpfJE6+08ru9gGQJUQExLRMYgYHa/d7mVFop/nNx3jkwXtpa205ousQktEDXwIyHFZaByLs7RokHI2RY9Mxb1wh37hwIl86ayxJrZH/vmEh2ZkuRPG0+rX43KgoyiQSPLR+ITjYy/jK/JMQkUKhOJ2cVp/cxcXFvPzyyyxdupSXX36ZoqIj64oeqfZuP1rdUPemIAgUX3w/v1ndQXcwgc2gxSdbyTWlKLELZFtUZNs0lKWp8BghU+1H1OgQtQYGO/fR1NFGX0QmGE0AQzODbdrfy9Lqdvb1BOgcCFKcbkCSYXd7P2q1CovVzqa9zeRlZXBOZQHjCzK4vMLB4mceOaLrMLiy8YejBAIBXn9/LddPyef80QUMxMFu1CGkEoQCPqR4BLvJgM1kQAgNHvP7qRiZL193OZpANd7u5qHHL7JMf2cj1lQzl10672SHp1AoTnGnTZf7iSQIwxclsTo9lF33G15/6xHGEMAvG7CoQmhVQ8ucygioRXAaYCAcwyCkCPW3oDc7iRqM/H1HA1IswugsC7vbB9jT5ePXV0xGkmVe295CdecA104t5f+WVyOJ/Ujb+nA40vje5eM/FpNAmhTG5/ONeAa32ZdcwQ9uuZJyc5L2rl4Wx4M0eyPEBRUWIcmYbDvecAJUGuaOzaetqRFUrmN6LxUjp9Vq+fEP7mHt+o1s2LwLELh47iQmThhHdU0tNXvqyPK4mT5tirLKnUKhOITyqXAYY0bl8lGzF7P14PN5rVbPpLm30bHoPtRClFHZbtbUd3POKM9Qa0qAQCxBuy/GxBwBtecs6nrDRKNhulUV/GGfF2F9NReXGPnazDLUKhFBgOumFPKzJTv5w4oarpiYT5YzDdHmYkN1A6t37uWssQdnEYshoNPpRnwdy996lQvHFrJ9xw4WjsslFEti16spy7ASS0okUjK+aJKS7HTMeh3N7f2oyyYd03upODKCIDBzxjRmzhgqjvP5fHzvx78jIrqxOLPZ0NDGy2+v5vYvX0pVZflJjlahUJxKTqsu9xNlwfw5iL4aQoGD3c+pZBJ/+07Gzl5IPDJATzCGwZLOq9vaWFbTwes72nltZw8RwUhlphHTwDZ00TbyiyeQmVtKum8raUKQG6YWoRKFoS8BkoQ/ksBp0nJWiZtMm5FwCpzuTKZVFrO/uZVEMgVAnz+M6C4+okrnttpdvLf2I66dlI9GJbKv28es0gxcFj1mnRqTVsWkAifr97WxbE8HDQEJT3bOMb+fiqP3mz8+jS5rKs6cMnR6E2muXNKLz+LRZ94kFoud7PAUCsUpREnoh6HVavnJ9+9mrCdMrHMzkY7NWOO1fP+b1/Kt7/6AaMG5bGrswhcIMmFUGUWFZeRkZNIc1ePKzOOq7/yCSWNLefiKEnIG3kW7/VF+d+UERmfZ6fBHUIkCT6yu49fv1fDWzlYa+kLYDWq6gzE8+SWoVSoysrJxudy8VN3HksYg+4wlXPe1I6s+b2msozzDikqEdJOOpDT0KEEUBKKJFClZRqPTkxI1zJo6mTFlJbizlIR+qmhvb8cb1aNWD58XQBAEjBmjeXfZipMUmUKhOBUpXe6fQKvVcv01h1+e0pHuQDPrZlZ37WX95jZEZER7PhPPv5j6HUuorBqNfNWtrFr0N7SpELMrMrHqtXx5WhGPrNpHZaaNHIeR28eUIQOjW73EkxJagwaTeWhWOAEQ7Rnc+Z1fHvXsYDkZ6cjeIL5wHLtejVoUSUoSwViSYCyB06zHqgaDKPHOqo0MmLP48X9POMo7pjjW2tra0RgPv9652eaguW3PCY5IoVCcypSEfgQkSeK5x35PRawat9nMXpOPRjGDnDEXIIoibc21TJ06DoCqseOoHPMb7rjjDpyx3QCY9VoWjM3lr+vq+e2Vk0n9s8U8LjuNl7c2U/mxQqd2bwBjfiX9fb2seP1F5FgQrd3N/GtuHPGymXZnJhOL0njtg43kW1R4wzGeXFuPWacm06onz2Fmxb4uqvLcZLudbPDpSaVSSsHVKSIvL5dkeDtwaK9JcLCf8aOURVoUCsVBSpf7EVj21mtUCT1cc/YE8h06ZhQ7uShPoHrbSnbuWEuGPc6993//wP6CIPDQQw/x4b5ukpKEVi1SlZ1Ghk2PIAiIooBKHPrv3NFZ/G1DA0vqBlnSEKDTPY5Zcxey5PGHON8Z46JcHVO1vTzz25+QSqVGFG96yRjQm7l2/myagxLnlGZyfrmHiXnpiKKKJ7d0UjV2PFOmnEF+cRlTss1s/Wjj8bp9iiPk8XhIN0RJJuLDtsuyTLhnNxddcO5JikyhUJyKlIR+BLr27SLbYUEAigvzKCvMpDzHxtiMBM8+8Qt+9ZtfIQgCkiQhy0Otb5vNhiZ/LL9bXk1dt5+9XT4aegJ4QzGQISXJSLKMiEBSkvClVNz4nZ8y/4pr+WDxIi4c5Ub1z4lejDotE9JktmxaP6J4L73mBuq0+fx1+RZmFruwmo0UZ7kpcFqYkJtGyDfInxcto7qpEwCDRkUkfOhKbYqT59vfuJlE92Z62/YSCQcY6Gmlv2EV37jlsiMa8aBQKD7/lL7Vz8Cg15OT7aE4kYbdbue1N5fw2pLVRJMi6WlWKgrd3HrzNTz+/KvU1tZy13ULGOPU8d35k/nd8mpuml5MWYaVFm+IP6+uY3ROOpF9H/HXR37PbXd/m2QkiN48vCDKaTHQOMLV40RR5Oqv3E5ncyO62F5sZgPRSBitSsCg1lLhsVHlsfPSOysovvUadvdEWHjDtONxqxRHyWq18vMf/Re1e/dRU1uHJyOPqWdcfmDVQYVCofgXJaEfAc+ocbS2bRxqpQsCAtA1GMRVPJb/vv/H1HTrsGdORRQE+hMRPmpJ0vqLP/LzH/0X5eXlvLV2G0/+/tcs/+gD7GYTf1y5F5teQyCW5AcXjyfNrEdnMPHKlqXs2DKL3IpxNNYsp8h9cA3srW0DnHfxWSOOecOaldRsXseEShtSPIpakFGLKmRZJhBJ0OELce3kfH7x/Ltc+fV7R/x8XnHiCIJARfkoKspHffrOCoXiC0tJ6EdAQOa5D7ZRYEiS57QwqLKhzh9LtiOTLXs3kjv2woM7a7QkE3E6Ig6WLv+A+XMvxGg08s3vPUAi8X2e+tPv0S19iXFZJsZmWrEatKi1ekRR5NxRHjYufZ2vfe9nPLdvD+11DXhMKhr9KTwTzycjI2NE8a5f9QGvPfwTLq908tLmvdx6ZglmrRpBgB3tg+g0Ig29ISYXuEnoJOZdfvVxunOKE02SJFavWc/GbTUgw5QJFcyaOV1p2SsUn2NKQh+hjWtWQd0avnfl2fT5Q7T3eOkYiHL1VTfw3Qd+TVru2EOOUWm0JBJGtu9uYP7cg9s1Gg233XMvb2Rns/WlR5hVZkaj0SIIAvFkEo3RgmYwiCAIfPmOb9HT00NHexuXlZSOuAXd1tbK9reeZaLHxChPGq9JEot3tGDUaRAAj92AShRxWzVsbR1k/rW3HqM7pTjZotEoP/7lH4npC7C7xgDw5vpWlq74Xx64/66jHgapUChObUpCH6E9m1YxJ8cJgNNqwmk1URZPsOrdN4nGZWStfNjjZFREP2FGr0uvvJYNK96lOxgh3SQQTcmk1AZyMrNIBQMH9nO73bjd7iOKd/W7b3J+ZTYr1nUAkJtmIhZPkowm0KtFegMxLhmTyz8272d9t8Q/vnTDEb3/ieL1ennnpedI+HqQRTWF46dx9uyLjmgp2S+avzz1D0TXBOx604Ftaa5cYlEHjz/1Al+5/gpqa/disVioqChX7qVC8TmhJPQREmSJfx8UoFWrGOz2kogG6PdWY7C4DhnDHfb3Mmlm6eHfUxD43kN/4K8/vZcLs62km8zotFp2tPZTNu3Cwx4zErIsU71zJztad9Pt9bGnqZ2peXaa+0MsGJ9Htz9CIJrgg7putvYneWnF+lPyQ93n8/H87x7kknInujQjAA17VvBqZztX3vjVkxzdqSmVSlHfNkB60aHzvGu1Rt5dvp19rX601hxSiSia+BKuvvRspp0x+SREq1AojiUloY+Qs7Cczt5teOwHu7xX7mqg35/gLJeK99q1DLbvxeIuQvfPLs14LEysewvz593+ie9rtVq59r8f5N2X/gbeAVKqFBXTLmbG2ecdday/+8n3GRVvYuKETFSih6dX7yXNpMegVfPCR/txWfS0D4TxxiUuve5mjEbjUZ/reFr22kvMK3Wg0xz8NS3OsLN/7w78fj9Wq/UkRndqikajpNAc9mf7m1tJqtNJyxmD7kDrvYRnX19HhstJYWHBCYpSoVAcD0pCH6E5C67guUdbqa9rxmNSsT8gsbPdx7fnTSaZStGxdA97gxIdvfVozenIyRjaVB/PPvZLNJrDf8D+S6Yni5u/df9RxxYIBGhrayMvLw+v14uqaQtnjc0hFo2QisfJTjNi1WvQqkUWTiwknkzitgSoixoQA/1Hfd7jLeLtxJitPWR7sU1D3d5aJk054yREdWozGo1ohMQh2xPxBOGYhCAn0GiHL/DjLJjIy28s4zv33HaiwlQoFMeBktBHSKVScdM3vk1PTw+dHe1cnJeP/PsfAaBWqbhnTiUf1Xeyut7PoMHI/HkXMG/O+Wi1hyakkQiFQry/5A38fT2UjJnI1DPPOmy3+JsvPodv7yayjQIbQ9AcgnOzh9ZL1+kNSFot4YSE3ahlIJwgzagDdKyq6+X8mZPYOxBCkiRE8RScY0ijR5YTh1y3N5qi0qms2344giAwutRD3cAARkvage3+QIB4LIrVYkQUh1e6q1RqvH5l5TaF4nR3Cn6Kn9rcbjfjxk8gLS2NmHgwWYuiyNSybOaP93DXrdex8NK5nymZ//WXP6DUX8P5aSHim9/g+T//4ZD9GhvqkRo2ccGoTCpzM5hTnoEj0k1Dj/9jcamozEnnrZ1tpGQZWZZZ19BDd0xFkcdJSmM6NZM5cMZ589jQ2DNsWzyZpDlhoLCw8CRFdeq7+YarsKWa6GmpJpVKkkolGejcx2DLJkZPmnXYY9SqU6+GQqFQHBmlhX6UBEHAUzmZhs7tFGcMTfwSiSfY7Vdz16QjKzDyer0s+uufEPy9SGotfVGZK0qtWI1DXaNlnnS66urp6ekZVu2+Ze2HTMsf3lI9d0wRv3xpOTNK3Oj+2dU/KtvNoj0DbPuoh2RoH550O6NyM3l+zW5mXH3k3azL336Dpm1rERMxUnoLUy64lPGTj333d3nVaLra57B4/XvkGFIEE+BV2bj2zvuO+bk+T1QqFd/79h00NDSy7IM1AFx3+1weezaFSnXon3woMMi4kqwTHaZCoTjGlIT+GVx85XW8v8TMkl2bEKQEGkc2N3/720dcMf7io79hfoEBTW4mAEvXbmZ/RwbjSnIP7JNpFOnq6hyW0N3ZuXTt3Uduuu3Atr5gjBv++4f89aWnyRSCCECnZOKu//dHNr7xLFOdMkTDyMD+sEDQN3hEsb731mtoGtYxv/Rgd+6H7zyHzmCgomrMEb3XSJxz4VxmnncBra2tmM1mXC6lq32kiouLuKO46MDry+b089LSbbgLxh/4HQ0HfQgDu7nyzrtPVpgKheIYURL6ZyAIArMvvhQuvvSo36Onpwe3HESjNh/YNqksj7c27RmW0Ot9Ka4rLRt27Jlnn8dja5Zj1oVJMxvp84eoiRj4+pyLyc3NY8/uXZRUVDF+/ASeffT/uKTCifZjw+pygSVbPiR+4bwRPx5o2r6WS8ocw7bNKs3gnXffOC4JHUCtVitd7MfA2WfNIMPt4tW33mMwmESjFqgq8nD117951I+HFArFqUNJ6CeZSqXi3xdDdThd9NHCuvouMk1q9g4kKJ11ySEzfAmCwJQLF7B05XLSgiKu3PFctfAc/uf26ynTBMiyalnz/t95x1lKhtWE1nHoLHMFRomGhgYqKio+NVZJklAnDy2eEgQBMaGs0nY6KB9Vyg9GHX5eBIVCcXpTEvpJlp6ejk/vIhSNYdIPLYe5sbGHW+57ALvDSXdnO1dXVGEymYYdt6+2hvdf+AuVdoHpZhXVfV7MVRP48y9/xBW5kG4Z6povy7Czt6uNFbUS84oPfc4dSoLZbD5k++GIokhSc+iYdUmSwKAs6qJQKBQn06lZ3vwFc8Nd97Ihks47DT4W74+Qfc5VVI0ZR3Z2NhMnnzEsmcfjcVpbW1n67GMsrHAyKstJvjuNeZUZ1H+wiFRXPemW4S35UreVvr4+Wvr8w7anJInWpJ7c3FxGquqsi9jc1HfgtSzLLK3t5rwF1xzl1SsUCoXiWFBa6KcAg8HA9V//9KKkd19/mY7ta9FEBwm2NLNZG2ZK+cGip8l5Dtr27T7kOFEUyHLaadDl0rh3L6NcBryhOHsCKq66894jinXG2eex1WDg7ZVLUadipHRmzr3xbvIKlGfcCoVCcTIpCf00sad6F4k9a5hXkcGAV8WUtBTrGtvo87hx2oa6zJ1WE60hgXgiifZj06X2BaKkFVVx3a130dfXx56d2/G43ZxXNeao5nCfeMZ0Jp4x/Zhdm0KhUCg+O6XL/TSxZfUKJhcOPRe32OwE4hLTCl1srm08sM+25j4W3vZfPL+jm05fiEgsTkOvj1frQ9z0ze8A4HQ6Oeu82VSOHntKLsiiUCgUiqOjtNBPE1qdnlhiEL1Wg1qlwpDmor2tBbXagCRJbG/pxe8cxXULr2DqzLN57e9PMdDVRn75WP7nB9ceUlT3cYFAgMUv/o1YfxeySs3oGeczZcbME3h1CoVCofislIR+mjh73kKW/OmnzK0amtEr3eli2f4grtJxfOCHSZcspLxyNDDUCv/at0Y2m1oymeSZ3zzIvGILxoKhYrpta19lVTjErNkXHZ+LUSgUCsUxd1ok9Pvvv59169aRljY0O9mcOXO44447TnJUJ1ZGRgbTr/k6S5a8ihALIhtsXH3Pj8jJzfvEY/x+PzqdDp1O94n7rFn5PtMyNBh1BycWmZDnYvGG95WErlAoFKeR0yKhA9x22/9v787jo6rv/Y+/zsxkMtmH7HsChJBAEGRVUajsYoCkqLQs1RaX1lavC7fq7W3rletttT+r3mK9xRUFcWMVEUWQRZBN1iBrICEhCyHbJJlkJjPz/f1BCcQEpGGSmYTP8/HwATnfs7znjMwn33POfL/3M3PmTE/H8Ki0vhmk9c34wfVKS0tY9vrfCHFasCkdpvg0pv3il63eMy/Oy+VHrQw4o2+sRykl99mFEKKTkIfiuqClr73MpB5+3No7hglpUaTYT/H5yqWtrtsjvR+5pS3Hc3f6BkkxF0KITqTT9NDfeustPvjgAxISEnj88cfp2bNnq+tZLBYsluYDqJSUlHRERK9QVVWFrqKA05oeTbnA4ENUfBIHc78DprZYf8iNw/m/DZ8TUl1LREggTpeLTcdKGDhhRseHF0II0WZeUdCzs7MpKipqtW3r1q08+uijREREoNPpWL58Offeey9ffvkler2+xfoLFixg3rx57R3Zax05dJCyogIiE3oCepRSFOYexRXUp9X1dTod9/77H1j36Ur25B9F8/Hn5pmP0r1H678wCSGE8E6aUkp5OsS/atiwYSxdupS4uLgWbZfqoc+YMYN169YRHx/fUTE7VF1dHYv/8RIlezfTWFOJUadxx7BUjAY9qw8UEH/7fUy5YxpFRUUcO5RDz97pxMdf+ZCvQohrT2FhIaNHj+7Sn51diVf00H9IaWkpUVFRAGzevBmdTtf08/cFBwcTHBzckfG8wgfzX2ZUeCPl6TFEmGLIO1PJ/67LIT0pjsjYBKJiYvn43Tfg1H5SQv3YsW0Vm6N685PZD8q9ciGE6AI6RUF/4oknKC8vR9M0AgMDefXVVzEYOkX0DtHY2IhWVYx/bAz1wWasNWdJjgylT3w9U0ffyIqcYvqHRVC88SNuTIkGIDo0mD35xzhy6GDT99eFEEJ0Xp2iKr799tuejuDVzvWwz/WyQ8MjKGmop662hrKaBlYcruCmqT8n97sD9I0xN9uub1woO77dIQVdCCG6gE5R0MXlGQwG9JHJVNdVEhLgR0x8IqWVNSQMH8AvHp6DpmnodHryv/iGfv6mpu3yyqpJumGMB5MLIYRwFynoXcRP7vsNH731f9QfzkPTNIISUrn73tlN98f79LuOrWvD8CuppGeUmfyyag42BPLLYT88a1p5eTmfvv82jsoS0OnplpzGpLtmym0PIYTwIvKJ3EUYjUZmPHDpOdU1TWP2o0+xY+vXrP9uP4kZN3D/iB/94ANx9fX1vPfiM2Slh+MTfm7o3bOW47z79xf5+cNXNl68EEKI9icFvRMrLy9n9Yfv4qqtRBdgZuK0nxEWFnbJ9XU6HTfcPIIbbh5xxcf46rNVjEoKxMdw4Tv/4cH+BJ0ppLi4mJiYmKt6DUIIIdxDhn7tpOx2O++9NJeRwRbGJ/oyMqSG916ai91ud+txKkpOERbccurVpBAjebnH3HosIYQQbScFvZPa9vUmboj2xfjP+9hGg4HhsSa2bFzv1uMEhUVRXVffYnlRjZ24xGS3HksIIUTbSUH3Ularla+/Wse3O7bjdDpbtNdWVRJkMjZbFmgyUltdid1uZ9eO7Rw+9B1XOxDgqIlTWH+iutl+aqwNlBrCSEy89NStQgghOpYUdC90cN9e3v3TE/h99znWbz7i1Wd+S1VV8xnRbvjRaLafqmy2bFt+BZGxibw5dw62rR9yatV8Xpn7FLW1tW3OEhQUxKQH5rC6wMmnB4tZdaiMHfZw7n74iTbvUwghhPvJQ3FeRinFlpWLmZJx4WGzxDAHnyx+m1m/eqRpWWhoKGlj7+ST9Z8QrNmpdvnQb9RU9ny5gil9o5qeXk+x2Vm5+G2m3/ebNmeKT0hk9r//QeZHF0IILyY9dC9js9nwd1mbLfP1MeC0lLdY7+iBPWjORiptipDEVPoNHEIw9c2Krr+vkcbKM27JJsVcCCG8l/TQvYyvry/1mm+zZS6XC3ybP2n+4ZuvMsxUQXCfc5PUlFTls3blEqzK2GJbZfRv39BCCCE8TnroXkbTNPqOnMhXh4uwOxxU19WzPKeEsXfMaLaerfQkwRcN4xptDqQy7zA9h93K1uMlOF0urA12PskpZlT2Tzr6ZQghhOhg0kP3QjeNHIhu2RQAAB59SURBVEVSSm+2rF2NX3Ags57MJCgo6HtrtfK7mKbj1gmZHO+ZysYNX2A0BXDn4w8TGhraIbmFEEJ4jhR0LxUXF8dd99x3yfaQ5D4UV50gxnyu0B8rrSQh4yYAUnqlktIrtUNyCiGE8A5yyb2Tmvqz2eQHp7P6RC2rT9TR0GM4YzKzPB1LCCGEh0gPvZPS6XRk/fRnno4hhBDCS0gPXQghhOgCpIfu5XZ9s4W9G1ejd9ghKIwf3/MrQkJCPB1LCCGEl5EeuhfLPXaU3LXvk9k9gNt6dWN0uJ1F//vnqx6fXQghRNcjBd2LffPlp9zSK6rpZ6PBQIKhjoKCAg+mEkII4Y2koHuRxsZGDuzfx/Hjx1FKoVwtx07Xaf8cOU4IIYS4iNxD9xJ7dm5jx6rF9A7WqHfC5/U+DBwzha1ffcjwXtEANDqc5Nl8mZiU5OG04mo5HA70er2Mjy+EcBsp6F6goaGBXSvfY0pGdNOyfk4nn339JX1vyWLV12vRORpQgWFM+/UTUgQ6sU9Wr2Xz9hxq7Rq+ehd9U2K4Z+adGAzyT1EIcXXkU8QL7N6xnQFRpmbLDHo9poYyrh96IzeNHOWhZMKdPlzyCduO1mFOvJHAfy47XlXFX16az1NzHvRoNiFE5yf30L2A3qDH0cp9cYVMWdpVNDY2smX3McyRydga6miw1gLgH2TmtMVAfv4pDycUQnR20kP3AgOHDOP1zz4kOfLCQ3D19kYcwdGYTKYf2No7KKWora3F398fvV7v6Thep7CwkLMWJ4fWrQSDP6CBo5aUtOvoFtWTbTv2kJSU6OmYQohOTAq6F/Dx8WHcrF+z4v3Xifax0dAINX5h/PRXD3s62hU5uG8vW1a+R5BqwIoPcf1vYMKUOz0dy6vU1dWRf+oUSf1va3bV5ejR7XRPqMWc2tOD6YQQXYEUdC/RM7U3PX7/PCUlJZhMJrp16+bpSFekrq6Orz9+gykZMU3L9hzZwp5dSVw/eKgHk3mXLzftIK7XEBz2emxWC6YAMwajiageQzi2+2NG/nGWpyMKITo5r7mHvmLFCiZNmkSfPn1YuHBhs7b6+noeeeQRxo4dy4QJE/jqq688lLJ9aZpGTExMpynmADu2bmZorH+zZQMSIzjwzYZW11dKYbVar7nR7krKLFSd/o78wzuoqign/8guTuzfQENNBclJCZ3m1ooQwnt5TQ89PT2dF198kfnz57doe+ONNwgMDGTt2rXk5eUxY8YMvvjiCwICAjyQVFzMx8eIw9m8OCulQNO1WPb5io85te8bArVG6pQPMX2HcvvUadfEg3979x/AnHo7ET5+2Ox2nE4njQ11WMuO0Cc9+od3IIQQP8BreuipqamkpKSg07WM9NlnnzFt2jQAkpOTycjIYNOmTR0dUbRi6E03s72koVmPe2tuKTeMmdhsvS9WLiXw1E4mp4UzqncMk9LCCSvZw+qlH3Z05A534mQeYbHpOBvtVJSeoMFSip+fLyGh4dicGqndIz0dUQjRBXhND/1yioqKiIuLa/o5JiaGkpKSVte1WCxYLJZmyy61rrh6RqORrAfm8OniN9FZq3EZTWSMnEpqWp+mdZRSnNr3DZN6hzbbtntECAdytqN+fFeX7qUfPHQEa309pQW7MJkTUaqRkoJ1mMNiCTGHkdZLHogTQly9Divo2dnZFBUVtdq2detWt33VacGCBcybN88t+xJXJj4hkXt/+/Ql251OJ74uW6ttAVojNputS99DPpVfSL3yp0f/wTidLux2O6FRSVTkf4u5WyiRkeGejiiE6AI6rKAvW7aszdvGxsZy+vRpQkPP9fCKi4sZNmxYq+vefffdZGdnN1tWUlLCjBkz2nx8cXX0ej02fesFu04Z8fX17eBEHev02VqCu0VRdSafRruVoNBYTP4hRPccQunRlSQlPebpiEKILqBTXHKfMGECH3zwAf369SMvL48DBw7wwgsvtLpucHAwwcHBHZxQXI6maXS/fjiHj28hLebCE/zHSqtIGnCTWy6322w21q7byNJPVmOtszJm5HB+fs9MrxjkpqiknNLifPCPxuhnpijvMM4GC4mpA7guo6+n4wkhugiveShu1apVjBgxgjVr1vDyyy8zYsQIjh8/DsDs2bOxWCyMHTuWBx54gGeeeYbAwMAf2KPwJmMys3Cm3sInR6tY810RnxytoD75RsZN/vFV73vzlu3MevAP/GXRbop1AynV0vjfd9czfPwMtmzZ5ob0bWe32zmRX0h4yi2Yo3piCgwlNK4P5tg+1JUdIzIsxKP5hBBdh6augS8EFxYWMnr0aNatW0d8fLyn41zzHA6H22YXKy0t5Q//byF51QH4m2OblrucjRQd2YzRWcH65a9x7PgJPv1iM1abA3+TgUnjR9InvbdbMlzOp2vW8tqKQzh8o/Ax+jVry925lAV/fYTrrx/Q7jmEaAv57OxcvKaHLq4d7pwqdMnKz6lRYRj9w5otr6suo66mipoGPWNv/wkvvPEZDUH90EcMpD4gg1fe28CylZ9d8XGcTiff7t7Lho1fU1FRccXbncw7jTEglMaGWqyWCux2G3ZbPfU15QSZI9m99+AV70sIIS6nU9xDF+JSqiwNWG06DH4XHqwrOfEtOoMvyf1vA5cDa3UJBWcsGPwOkdCjDzqdjqjk/nz5zXZGjbyJkJDLX/besnUHH326GZdfLAajHx9/sZge0b48/Kt7fvCXk+TEGD7avIfQuD64nA7sNiuapuHrH0ydpiivrnPLeRBCCCnool04HA4WLl7Cd7nF2J0Q4q8n67aRXD/gOrcex9/PgNEADY5G9AYfaqtKUQrC4vrSaKtDuVz4BYQQFtWHE4c3EZPYC4PBB4CQuAxWfbaOGT+59H38vLx83vtkG1Epwy8sDIvmTE0Vr76xiIceuPuy+Roa7JTl7aFbbDo6vQGT/7kHNhtqK2i024gMN1/9SRBCCOSSu2gHSime/cvfOVxuJjDhBkKTb0AXMZg3lmxl6zc73Hqs7NvHEGKwYLWUUld9lrJTOXSLTcfRaKPBWo2t3oLRPwSn00lITDqncnOatvU1+VNtqbns/j9a8QXh3Qe2WO4fZObYqWrq6i7fwz5xqpSeKank71tNZfERaiuLKD2xE0vpEfRaI+NH39K2Fy6EEN8jBV243d69+6lwhOEfdOFStqZpRCYPYMXnW906MUtSUiJD08OoLjyATu+D3mBE0+mx11vQ6QzodeBSYK23o/fxx1p7oYBXluYz6PrWvzZ29uxZ9u7dR9GZKvT6CxeylFIUnzrGzs1rOHA4l0WLP8Jut18yn9Go4/qbxpEQF4OjthhnfTkh3SIwGRTxkUFERES47VwIIa5tcslduN3mb3YTGpPWalutw5fq6mrMZvddaq6orufWcZl8t38n1SVHCQyLJyAkCqOvH/a6Shy2GnwDwjhbcpw+vRIAcDga8Wk4xdDB05rty2q18td5b1FcraHzC2V/znHinfF0T4rDYDCwY9On6AJiCUkciqm+hoNlBn779Iv8Yc59TQMfXSxz3EheXLCOwTdPwFpbTUlhLr6+/kQMyCDQdsytDwgKIa5t8mki3M5oNOB0OpruVTej3PeVtfPKqxsITAgjrucA/KMyOHlwC90ik9E0DU1vICgwEEvlGUpP7CKtZxylJ/cS7m/nP+c80GJQm+dffh1HSD+iws59xaxH2kDKa2o5fqIAXcMZTBFp+AeFgwIfnYuwyDgcoZG88tpifv/Er7FarSxduYajeSWgFMlxYQzoEcDeE/sIT+hLj7SB1FSewXp6J//+xC/deh6EENc2KejC7SaO+xHPzf+UqO7Nv1+tlCLMX7l9UCCd7lxRdjicGH39ie1xHacPfUVQeHfQNJy1xTgbzjLgugym3hJDv4wxREVFtdhPXl4+Z+v9iYy88H3x5F7XUbZpNZX13WioLiIhIw2nw4HTZqFn8rkJgwwGH0osLgoKCnjh74swRV+Pf8S5++7HLXXUFO7knqmj+WZXDrYGJ4Mykhg3+jF8fFr5hUcIIdpI7qELt0tMTGBgLzNnC4803S+3NVg5c2wzs2dmuf14qckRNFhrMQcH0mivJ6hbNKkDx+FnMlJ3+lvSevfkljHZRIUF8aORI1oU8+rqar5cv5EPl6zALzimWZumaQwZMZGkuFDqLaVojRaCfZ2k90rGz+/C+PSaIYDXFnxIcOJN+AdeeHbA5BdAaI+b+WDZF8THRjF0QDrjx9wqxVwI4XbSQxftYvbdP+Hb3Xv5cuMObI0ukiOCufOp+9167/y86Xdl8ftn/4ZP+HVozgZQfricjVgr8rh57FRCQiOprTpL/7S4Zpf7lVL8481FHDxRhdGcSPGpRipqD9KjVzrRURceVtM0jW5hUfRKiqRXz6QWx3c5XZw4up99tVaie8eg1yAo0ER8XAxORyM7N6/B5tTQRWg47KV8/NmLzJo6mqGDWz49L4QQbSUFXbSbQQMHMGhg+w9rajKZmPu7h1iyYjX2M0Xs3PUFeh9/hv5oMib/QM7k7yc5HH42/d5m27334XJyK4OI6JECQJA5nM1rl1NWVY/Jtxqz+UJPu7bkO37+00ms2nqc0NiUpuUul4v9Bw6g9H74BpvxDTg3+Uyt3cHR43nUlB4hKH4gQS4nRlMAgcHdIDyWtz/6it69ev7goDZCCHGlpKCLLsFkMlF6upCPln6CnzkWTedg8evPE9vNhw8XvU5SUmKz9V0uF7sOnKBb9wsDxuh0OvpcN5ic/d+ibCmYzf2wNdRReWofP518M7cMH8aZ8uVs3budgMjeGHyMHNm3lZqqSm4clcXX61aQf3ATTkcjsSmDUEqjutZGQKwP+Qe+pqIwANCh17mIjU9i2arPuWfGXR18poQQXZUUdNElvP7GW7y84DN63TgDncEHX/9gdDoDeftW8/iTf+DjxW83W7+urg47fi32ExGTyPDQCA58/TEBVh2JoUFkz5zd1JOeflcWk26rYd2Gr7HWV2ExK64bdiffrFtGcVEhYYkD8PE3cWT3OhqtlUQk9efYt2uJS7uJoJALU8eWFOSwc9cpKehCCLeRgi46vZMnT/L7P8+n39hf4WsKxuVyYrNWo9MbSO4/kZ1r/9ZiG5PJhM7Z0Or+jL5+9E3vzeMPzW61PSgoiKxJtwFw4EgRh/ZuobzWRdrw6TgcjSiXIjg8iYqiIxQcWk/voVOaFXOAbrHpFOevRSnllvnghRBCnnIXnd4vH59LeNL1+PqFgKah0xvwCwpDuZw4nY34m+PJyclpto2Pjw/JMcHYbS2Lem1VGdf3Tb6iYwf76zm0fxexvc9dujcYfDD4+KBpitCYXjQ21OEXHInVWk+jvbFpu8aGaiLjUykvL2/7CxdCiItIQRedWm5uLnZDZKu9XKN/CHZrNS6XA19f3xbtv5z9U2zFO6guLwbOPfV+tiiXEFc+U7Myr+j4k2+7FafSoWkX/ilpmoamaRQe+oqg8ETs9TXYGqxYG2xUVVZgrysnKS4SnU7J19eEEG4jl9xFp3by5El8AiLQqutotNXh4xvQ1KbT6XEphbXyNL169WqxbUBAAH/642Os37CZvQdz0GmQOXEQAwcOuOLL4H37pBFgdOFyOtB0ejRNQylFSe4uonoMobY8H9VoIdjcHbutHnQGAv31BAUFYqpzylPuQgi3kYIuOrWMjAxs1Z+S0u8WDu1aQ2LGmKai3mivJ2/3J8zMHnnJ7Q0GA+PG3Mq4Mbe2OcPYW65jd3EJfiGxuFwuNBROez0G3wD8As1oDWewVvsRHH7uSXuLpZQzx7bwyH2XnrZVCCH+VVLQRacWHR1NVIAdp3KSPmg8xw9sPjc6naZRWXiQ2XeN5umn/7NdMzz9n79l6oxfUm2tJCQ6HbtDYbfXY60qITImCYctHK2+hIqT36DQaLSc5u/z/4fIyMh2zSWEuLZIQRed3tv/+AvTZz9KlaMbib36Y7dWU3/mIH9+4T+YeNvYdj++Xq9n2eL5vL3gXV74v7fw9Q9B53ISk9ADTdMw+BixazB0wA3o9DrqCrZJMRdCuJ0UdNHp+fv7s3zxPzh8+DBr128iMT6NSZmPodN13DOfmqahdEayfvYYRl8/TucdpqDoCKFx56aR1RkDOFtejtFVy7ibruuwXEKIa4cUdNFlpKWlkZbW+jzsHcFSa8Xoe26wmrjkNOqt31J0dAt+3c7NwW49dZQZ2SOZMG6UxzIKIbouKehCuEl6r2TW7ikhOCwagJQ+g+ie6uBM8UmqSk/x2CPTGTJ4kIdTCiG6KvkeuhBuMvrWETRWHsblcjYt0xsMRMQkkxjpy+BBMruaEKL9SEEXwk0MBgO//c3PqMz9iqITe6k8W0xp3j7U2d38x2P3yRCvQoh2JZfchXADpRTvf7yCnftP4vQJx15TCXUF/Ornd9G/f39PxxNCXAOkoAvhBv94cxFbc8rRGXwJj4ojMnkATqeD1977jP/p3p3g4GBPRxRCdHFyyV2Iq7R7z17eXbKeygYjNc4QDuQcZNuGT1BKEZI4lA+XfurpiEKIa4D00IW4CjabjWdfeof4AVPw8TEC4BcUir2hlr3b1zN4+HhOFsqMakKI9uc1PfQVK1YwadIk+vTpw8KFC5u1Pfnkk4wYMYIpU6YwZcoUXn31VQ+lFKK5L9ZtIDi2f4sH3oymQOobnDQ22jyUTAhxrfGaHnp6ejovvvgi8+fPb7X9/vvvZ+bMmR2cSojLyz9VTEJSCodzC8HQfOY0g18Ilsoy+sWHeSidEOJa4jUFPTU1FaBDh+sU4mr17J5A/oEKgvx9qGu0YfC5MO96Y30V9rJ67vr1wx5MKIS4VnSa6vnWW28xadIkHnzwQXJzcy+5nsViobCwsNl/JSUlHZhUXEvGjBpBQ9l3JMbHEuIHjdZK7FYLNWdPEUQZzzz1gDzhLoToEB3WQ8/OzqaoqKjVtq1bt6LX6y+57aOPPkpERAQ6nY7ly5dz77338uWXX7a6zYIFC5g3b57bcgtxOT4+Psx5cDp/e+0DjIZIYkIDqa8soGeKiccemofB4DUXwYQQXVyHfdosW7aszdtGRUU1/T0rK4s//elPlJSUEBcX12Ldu+++m+zs7GbLSkpKmDFjRpuPL8TlJCUl8pe5czh27DjlFRX0Tr2V0NBQT8cSQlxjOkX3obS0tKmob968GZ1O16zIXyw4OFgucYoOp2kaqam9PB1DCHEN85qCvmrVKp5//nksFgvr1q1j/vz5vPnmm6SkpPDEE09QXl6OpmkEBgby6quvyqVMIYQQ4iJeUxUzMzPJzMxste3tt9/u2DBCCCFEJ9NpnnIXQgghxKVJQRdCCCG6ACnoQgghRBcgBV0IIYToAqSgCyGEEF2AFHQhhBCiC/Car621J6fTCSBjugshxL/g/Gfm+c9Q4d2uiYJeVlYGIMO/CiFEG5SVlZGUlOTpGOIHaEop5ekQ7a2hoYGcnBwiIiIuOwnMeefHfl+0aBHR0dEdkPBfJxndQzK6h2R0H2/K6XQ6KSsrIyMjA5PJ5NEs4oddEz10k8nE4MGD/+XtoqOjiY+Pb4dE7iMZ3UMyuodkdB9vySk9885DHooTQgghugAp6EIIIUQXIAVdCCGE6AL0Tz/99NOeDuGNfH19GTZsGL6+vp6OckmS0T0ko3tIRvfpLDmFd7kmnnIXQgghujq55C6EEEJ0AVLQhRBCiC5ACvr3nDx5kmnTpjF+/HimTZtGXl5ehxz3ueeeY9SoUfTu3ZujR49eUZ62trVVZWUl9913H+PHj2fSpEn85je/oaKiAoC9e/cyefJkxo8fzy9+8QvKy8ubtmtrW1s9+OCDTJ48maysLKZPn86hQ4cA7zqX582bN6/Ze+5N53HUqFFMmDCBKVOmMGXKFDZv3ux1GW02G3/84x8ZN24ckyZN4ve//z3gPe91YWFh0/mbMmUKo0aNYujQoV6VUXQhSjQza9YstXz5cqWUUsuXL1ezZs3qkOPu3LlTFRUVqVtvvVUdOXLkivK0ta2tKisr1bZt25p+/vOf/6yeeuop5XQ61ZgxY9TOnTuVUkq98sor6sknn1RKqTa3XQ2LxdL097Vr16qsrCyllHedS6WUysnJUbNnz256z73tPH7//8WrydFeGefOnaueffZZ5XK5lFJKlZWVKaW8770+77//+7/Vf/3Xf3l1RtF5SUG/yNmzZ9WgQYOUw+FQSinlcDjUoEGDVHl5eYdluPhD9HJ52trmTmvWrFF333232rdvn7r99tublpeXl6sBAwYopVSb29xl2bJlKjs72+vOpc1mU3fddZcqKChoes+97Ty2VtC9KWNtba0aNGiQqq2tbbbc297r82w2mxo2bJjKycnx2oyic7smhn69UsXFxURFRTWN967X64mMjKS4uJjQ0FCvyqOUalObu16Hy+Vi8eLFjBo1iuLiYmJjY5vaQkNDcblcVFVVtbnNbDZfVb7f/e53bNmyBaUUr7/+utedy5dffpnJkyc3G9rTG8/jnDlzUEoxaNAgHnvsMa/KWFBQgNlsZt68eWzfvp2AgAD+7d/+DZPJ5FXv9Xnr168nKiqKvn37kpOT45UZRecm99BFm8ydOxd/f39mzpzp6SitevbZZ9mwYQOPPvoozz//vKfjNLNnzx5ycnKYPn26p6Nc1qJFi1i5ciVLlixBKcUzzzzj6UjNOJ1OCgoK6NOnD0uXLmXOnDk89NBDWK1WT0dr1ZIlS5g6daqnY4guTAr6RWJiYigtLW2a+9fpdHLmzBliYmK8Lk9b29zhueeeIz8/n5deegmdTkdMTAxFRUVN7RUVFeh0Osxmc5vb3CUrK4vt27cTHR3tNedy586d5ObmMnr0aEaNGkVJSQmzZ88mPz/fq87j+ddoNBqZPn06u3fv9qr3OiYmBoPBQGZmJgD9+/enW7dumEwmr3mvzystLWXnzp1MmjSpKbu3ZRSdnxT0i4SFhZGens6qVasAWLVqFenp6R67lHW5PG1tu1p//etfycnJ4ZVXXsFoNAKQkZFBQ0MDu3btAuD9999nwoQJV9XWVnV1dRQXFzf9vH79ekJCQrzqXN5///18/fXXrF+/nvXr1xMdHc0bb7zBvffe6zXn0Wq1UlNTA4BSitWrV5Oenu5V73VoaCjDhg1jy5YtwLmnv8vLy0lOTvaa9/q8ZcuWMXLkSLp16wZ4579t0QV45ta99zp+/Li644471Lhx49Qdd9yhcnNzO+S4c+fOVbfccotKT09XN910k5o4ceIP5mlrW1sdPXpUpaamqnHjxqnJkyeryZMnqwcffFAppdS3336rMjMz1dixY9U999zT9LTx1bS1RVlZmbrzzjtVZmammjx5spo1a5bKyclRSnnXubzYxQ+fect5PHXqlJoyZYrKzMxUEydOVA899JAqLS31qoznc86cOVNlZmaqrKwstWHDBqWU973X48aNUxs3bmy2zNsyis5Phn4VQgghugC55C6EEEJ0AVLQhRBCiC5ACroQQgjRBUhBF0IIIboAKehCCCFEFyAFXQghhOgCZCx3Idxo1qxZ7NixA4Dk5GTWrFmDpmnAucFaRo4cicViASA7Oxs/Pz/ee+89zGYz27Zta1p36tSp5OTk4Ofnx86dO/Hx8Wm2/yFDhrBw4UIPvEIhhLeSHroQ7SQvL69pDnGA5cuXNxXz84YMGQJAVVVV05zodXV1TXO419fXc/DgQQDsdjv79u0DYPDgwe2eXwjRuUhBF6IdnO9Rv/vuu03LzveoDYYLF8YuLsznh0Xds2cPTqeTsLCwZsv379+PzWYDLvwiIIQQ50lBF6IdpKamkpyczObNm8nLy2Pr1q3k5uYyfPhwAgMDm9aLjIwkKSkJODdpy8V/3nPPPcCFgn7+T4PBwIABAzrqpQghOgkp6EK0k1mzZqGUYuHChbzzzjtNy77vfC/9+4V76tSpREZGsnv3blwuV1OhT09PJyAgoCNeghCiE5GCLkQ7yc7OJigoiCVLlrBx40YSExMZOXJki/XOXz4vKyvjyJEj7N+/nx49ehAWFsbgwYOprq7m0KFD7N69G5D750KI1klBF6KdBAQE8OMf/xir1YrL5WLGjBnodC3/yV1coN98803sdntTkT/f9s4772C1WlusL4QQ50lBF6IdzZo1C51Oh7+/P1OnTm11nYSEBKKjowGa5rk+X7TPF/bzyzVNk4IuhGiVFHQh2lFCQgLbtm1j06ZNBAUFXXK980Xa4XAAFwp5r169MJvNTctTUlIwm83tnFoI0RlJQReinYWEhFy2mEPzr6HFxcURExMDtOyRS+9cCHEpmlJKeTqEEEIIIa6O9NCFEEKILkAKuhBCCNEFSEEXQgghugAp6EIIIUQXIAVdCCGE6AKkoAshhBBdgBR0IYQQoguQgi6EEEJ0AVLQhRBCiC7g/wNZPAsvQ2iW1AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Mann-Whitney U Test for statistical significance analysis\n",
        "def mannwhitney(descriptor, verbose=False):\n",
        "  # https://machinelearningmastery.com/nonparametric-statistical-significance-tests-in-python/\n",
        "  from numpy.random import seed\n",
        "  from numpy.random import randn\n",
        "  from scipy.stats import mannwhitneyu\n",
        "\n",
        "# seed the random number generator\n",
        "  seed(1)\n",
        "\n",
        "# actives and inactives\n",
        "  selection = [descriptor, 'class']\n",
        "  df = df_final_2class[selection]\n",
        "  active = df[df['class'] == 'active']\n",
        "  active = active[descriptor]\n",
        "\n",
        "  selection = [descriptor, 'class']\n",
        "  df = df_final_2class[selection]\n",
        "  inactive = df[df['class'] == 'inactive']\n",
        "  inactive = inactive[descriptor]\n",
        "\n",
        "# compare samples\n",
        "  stat, p = mannwhitneyu(active, inactive)\n",
        "  #print('Statistics=%.3f, p=%.3f' % (stat, p))\n",
        "\n",
        "# interpret\n",
        "  alpha = 0.05\n",
        "  if p > alpha:\n",
        "    interpretation = 'Same distribution (fail to reject H0)'\n",
        "  else:\n",
        "    interpretation = 'Different distribution (reject H0)'\n",
        "  \n",
        "  results = pd.DataFrame({'Descriptor':descriptor,\n",
        "                          'Statistics':stat,\n",
        "                          'p':p,\n",
        "                          'alpha':alpha,\n",
        "                          'Interpretation':interpretation}, index=[0])\n",
        "  filename = 'mannwhitneyu_' + descriptor + '.csv'\n",
        "  results.to_csv(filename)\n",
        "\n",
        "  return results"
      ],
      "metadata": {
        "id": "CsdEJ76_yJ0A"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#box plot of active/inactive compounds by pIC50 value\n",
        "plt.figure(figsize=(6, 12))\n",
        "\n",
        "sns.boxplot(x=\"class\", y=\"pIC50\", data = df_final_2class,)\n",
        "\n",
        "plt.xlabel(\"Bioactivity class\", fontsize = 14, fontweight = \"bold\")\n",
        "plt.ylabel(\"pIC50\", fontsize = 14, fontweight = \"bold\")\n",
        "\n",
        "plt.savefig(\"pIC50_plot.pdf\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 721
        },
        "id": "cXPq5nDNhHNY",
        "outputId": "66537887-a81a-4b39-f723-5163d45b0e3e"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x864 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Mann Whitney test for pIC50\n",
        "mannwhitney('pIC50')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "S7q9uaPTyfA9",
        "outputId": "4577d08c-f6fc-4a2e-83d3-060c2d97ad31"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-8d886fd9-0e27-47a0-ae46-0d28d63dc55e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Descriptor</th>\n",
              "      <th>Statistics</th>\n",
              "      <th>p</th>\n",
              "      <th>alpha</th>\n",
              "      <th>Interpretation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>pIC50</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.05</td>\n",
              "      <td>Different distribution (reject H0)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8d886fd9-0e27-47a0-ae46-0d28d63dc55e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8d886fd9-0e27-47a0-ae46-0d28d63dc55e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8d886fd9-0e27-47a0-ae46-0d28d63dc55e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "  Descriptor  Statistics    p  alpha                      Interpretation\n",
              "0      pIC50         0.0  0.0   0.05  Different distribution (reject H0)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#box plot of active/inactive compounds by MW value\n",
        "plt.figure(figsize=(6, 12))\n",
        "\n",
        "sns.boxplot(x=\"class\", y=\"MW\", data = df_final_2class)\n",
        "\n",
        "plt.xlabel(\"Bioactivity class\", fontsize = 14, fontweight = \"bold\")\n",
        "plt.ylabel(\"MW\", fontsize = 14, fontweight = \"bold\")\n",
        "\n",
        "plt.savefig(\"pIC50_plot.pdf\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 721
        },
        "id": "1NT4-bfVhHCC",
        "outputId": "aa3f3502-2e2f-465b-9ea6-842e783d399a"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x864 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Mann Whitney test for pIC50\n",
        "mannwhitney('MW')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "5cP8KV_Wytvn",
        "outputId": "27a9d448-6982-4f81-9afc-b1287a1c6ce3"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Descriptor</th>\n",
              "      <th>Statistics</th>\n",
              "      <th>p</th>\n",
              "      <th>alpha</th>\n",
              "      <th>Interpretation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>MW</td>\n",
              "      <td>1134598.0</td>\n",
              "      <td>9.345892e-15</td>\n",
              "      <td>0.05</td>\n",
              "      <td>Different distribution (reject H0)</td>\n",
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              "  </tbody>\n",
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              "          document.querySelector('#df-b3c9b803-8e06-4e76-a72e-29b72bf8a21e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b3c9b803-8e06-4e76-a72e-29b72bf8a21e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
            ],
            "text/plain": [
              "  Descriptor  Statistics  ...  alpha                      Interpretation\n",
              "0         MW   1134598.0  ...   0.05  Different distribution (reject H0)\n",
              "\n",
              "[1 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#box plot of active/inactive compounds by LogP value\n",
        "plt.figure(figsize=(6, 12))\n",
        "\n",
        "sns.boxplot(x=\"class\", y=\"LogP\", data = df_final_2class)\n",
        "\n",
        "plt.xlabel(\"Bioactivity class\", fontsize = 14, fontweight = \"bold\")\n",
        "plt.ylabel(\"LogP\", fontsize = 14, fontweight = \"bold\")\n",
        "\n",
        "plt.savefig(\"pIC50_plot.pdf\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 721
        },
        "id": "A2I0TWy_hG5k",
        "outputId": "26f5b2ec-42fa-4cc5-b40e-d911070f17f6"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x864 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Mann Whitney test for pIC50\n",
        "mannwhitney('LogP')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "x88qYmhLyxXP",
        "outputId": "5626249c-16ee-4101-dbb7-c20cd9018360"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-ce90cfbc-e3c1-47b6-a94d-309c127a74ea\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Descriptor</th>\n",
              "      <th>Statistics</th>\n",
              "      <th>p</th>\n",
              "      <th>alpha</th>\n",
              "      <th>Interpretation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>LogP</td>\n",
              "      <td>974762.5</td>\n",
              "      <td>2.096051e-41</td>\n",
              "      <td>0.05</td>\n",
              "      <td>Different distribution (reject H0)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ce90cfbc-e3c1-47b6-a94d-309c127a74ea')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ce90cfbc-e3c1-47b6-a94d-309c127a74ea button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ce90cfbc-e3c1-47b6-a94d-309c127a74ea');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "  Descriptor  Statistics  ...  alpha                      Interpretation\n",
              "0       LogP    974762.5  ...   0.05  Different distribution (reject H0)\n",
              "\n",
              "[1 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#box plot of active/inactive compounds by Number of Hydrogen donors value\n",
        "plt.figure(figsize=(6, 12))\n",
        "\n",
        "sns.boxplot(x=\"class\", y=\"NumHDonors\", data = df_final_2class)\n",
        "\n",
        "plt.xlabel(\"Bioactivity class\", fontsize = 14, fontweight = \"bold\")\n",
        "plt.ylabel(\"NumHDonors\", fontsize = 14, fontweight = \"bold\")\n",
        "\n",
        "plt.savefig(\"pIC50_plot.pdf\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 721
        },
        "id": "ueqvgP7BhGo1",
        "outputId": "8711bd31-194f-4859-fd54-1794a107253b"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x864 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Mann Whitney test for pIC50\n",
        "mannwhitney('NumHDonors')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "nAhG2K5Ey3zA",
        "outputId": "b8c5c2fd-1a6d-4e75-8289-88e1b1934adc"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-12440e4c-702f-4711-8da8-188c2ea28cf5\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Descriptor</th>\n",
              "      <th>Statistics</th>\n",
              "      <th>p</th>\n",
              "      <th>alpha</th>\n",
              "      <th>Interpretation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NumHDonors</td>\n",
              "      <td>1214938.0</td>\n",
              "      <td>3.972007e-07</td>\n",
              "      <td>0.05</td>\n",
              "      <td>Different distribution (reject H0)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-12440e4c-702f-4711-8da8-188c2ea28cf5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-12440e4c-702f-4711-8da8-188c2ea28cf5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-12440e4c-702f-4711-8da8-188c2ea28cf5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   Descriptor  Statistics  ...  alpha                      Interpretation\n",
              "0  NumHDonors   1214938.0  ...   0.05  Different distribution (reject H0)\n",
              "\n",
              "[1 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#box plot of active/inactive compounds by Number of Hydrogen acceptors value\n",
        "plt.figure(figsize=(6, 12))\n",
        "\n",
        "sns.boxplot(x=\"class\", y=\"NumHAcceptors\", data = df_final_2class)\n",
        "\n",
        "plt.xlabel(\"Bioactivity class\", fontsize = 14, fontweight = \"bold\")\n",
        "plt.ylabel(\"NumHAcceptors\", fontsize = 14, fontweight = \"bold\")\n",
        "\n",
        "plt.savefig(\"pIC50_plot.pdf\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 721
        },
        "id": "9JjOchLXxlZM",
        "outputId": "7f4de86d-4a5e-438d-ce59-7cd876071099"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x864 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Mann Whitney test for pIC50\n",
        "mannwhitney('NumHAcceptors')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "0bO743jZy8lQ",
        "outputId": "7272bf8e-d056-409f-f084-a4a58d83470e"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-ed16c227-5775-4824-a19c-f21ef13d0572\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Descriptor</th>\n",
              "      <th>Statistics</th>\n",
              "      <th>p</th>\n",
              "      <th>alpha</th>\n",
              "      <th>Interpretation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NumHAcceptors</td>\n",
              "      <td>1124335.0</td>\n",
              "      <td>2.287311e-16</td>\n",
              "      <td>0.05</td>\n",
              "      <td>Different distribution (reject H0)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ed16c227-5775-4824-a19c-f21ef13d0572')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ed16c227-5775-4824-a19c-f21ef13d0572 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ed16c227-5775-4824-a19c-f21ef13d0572');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      Descriptor  Statistics  ...  alpha                      Interpretation\n",
              "0  NumHAcceptors   1124335.0  ...   0.05  Different distribution (reject H0)\n",
              "\n",
              "[1 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Brief summary of the results**\n",
        "\n",
        "As expected, there is statical significant difference between the pIC50 values of active vs inactive bioactivity classes, due to our choice of thresholds.\n",
        "\n",
        "All 4 Lipinski's descriptors showed statistically significant difference between actives and inactives.\n",
        "\n",
        "Although these descriptos give a great macro-overview of the examined drug molecule, better tools are available for calculation of more detailed (micro) descriptors, e.g. PaDEL.\n"
      ],
      "metadata": {
        "id": "GiYWjBHB13gz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#compressing all the data into a single zip folder\n",
        "!zip -r data_and_results.zip . -i *.csv *.pfd"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hkbUiK6XzSeo",
        "outputId": "c7a0e434-d53f-4751-a85b-a69c552772f0"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "updating: bioactivity_data.csv (deflated 93%)\n",
            "updating: HIV1_integrase_inhibitor_bioactivity_data_preprocessed.csv (deflated 83%)\n",
            "updating: HIV1_integrase_inhibitor_bioactivity_data_curated.csv (deflated 84%)\n",
            "updating: HIV1_integrase_inhibitor_3class_pIC50.csv (deflated 79%)\n",
            "updating: HIV1_integrase_inhibitor_2class_pIC50.csv (deflated 79%)\n",
            "updating: mannwhitneyu_pIC50.csv (deflated 14%)\n",
            "updating: mannwhitneyu_MW.csv (deflated 8%)\n",
            "updating: mannwhitneyu_LogP.csv (deflated 8%)\n",
            "updating: mannwhitneyu_NumHDonors.csv (deflated 11%)\n",
            "updating: mannwhitneyu_NumHAcceptors.csv (deflated 11%)\n",
            "updating: descriptors_output.csv (deflated 96%)\n",
            "updating: HIV1_integrase_inhibitor_bioactivity_data_3class_pIC50_pubchem_fp_PaDEL.csv (deflated 96%)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Detailed descriptor calculation and dataset preparation using PaDEL\n"
      ],
      "metadata": {
        "id": "Q0Y9cQ-V0TQm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#downloading PaDEL descriptors\n",
        "! wget https://github.com/dataprofessor/bioinformatics/raw/master/padel.zip\n",
        "! wget https://github.com/dataprofessor/bioinformatics/raw/master/padel.sh"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qb-HC02HzSWe",
        "outputId": "9a266c65-a901-4ee7-d834-1ae36fe2fa66"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-01-14 11:56:07--  https://github.com/dataprofessor/bioinformatics/raw/master/padel.zip\n",
            "Resolving github.com (github.com)... 192.30.255.112\n",
            "Connecting to github.com (github.com)|192.30.255.112|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/dataprofessor/bioinformatics/master/padel.zip [following]\n",
            "--2022-01-14 11:56:07--  https://raw.githubusercontent.com/dataprofessor/bioinformatics/master/padel.zip\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 25768637 (25M) [application/zip]\n",
            "Saving to: ‘padel.zip.6’\n",
            "\n",
            "padel.zip.6         100%[===================>]  24.57M  31.9MB/s    in 0.8s    \n",
            "\n",
            "2022-01-14 11:56:09 (31.9 MB/s) - ‘padel.zip.6’ saved [25768637/25768637]\n",
            "\n",
            "--2022-01-14 11:56:09--  https://github.com/dataprofessor/bioinformatics/raw/master/padel.sh\n",
            "Resolving github.com (github.com)... 192.30.255.113\n",
            "Connecting to github.com (github.com)|192.30.255.113|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/dataprofessor/bioinformatics/master/padel.sh [following]\n",
            "--2022-01-14 11:56:09--  https://raw.githubusercontent.com/dataprofessor/bioinformatics/master/padel.sh\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 231 [text/plain]\n",
            "Saving to: ‘padel.sh.6’\n",
            "\n",
            "padel.sh.6          100%[===================>]     231  --.-KB/s    in 0s      \n",
            "\n",
            "2022-01-14 11:56:09 (5.16 MB/s) - ‘padel.sh.6’ saved [231/231]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#extracting PaDEL descriptors\n",
        "#! unzip padel.zip"
      ],
      "metadata": {
        "id": "VSCnbWqnzR2Q"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#preparing the dataset for descriptor calculation using 3 class dataset\n",
        "df_3c = df_final\n",
        "selection = [\"canonical_smiles\", \"molecule_chembl_id\"]\n",
        "df_selection = df_3c[selection]\n",
        "df_selection.to_csv(\"molecule.smi\", sep = \"\\t\", index = False, header = False)"
      ],
      "metadata": {
        "id": "yMwLH3f42nx7"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#checking the formatting of the dataset\n",
        "! cat molecule.smi | head -5"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MlGiJHs96Ivk",
        "outputId": "a6de4ff8-300a-4f9b-b0e9-fd2d93ff7586"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "O=C(/C=C/c1ccc(O)c(O)c1)O[C@H](Cc1ccc(O)c(O)c1)C(=O)O\tCHEMBL324842\n",
            "O=C(CC(O)CCc1ccc(O)c(O)c1)O[C@H]1Cc2cc(O)c(O)cc2O[C@H]1c1ccc(O)c(O)c1\tCHEMBL304722\n",
            "O=C(CCc1ccc(O)c(O)c1)c1ccc(O)c(O)c1O\tCHEMBL67076\n",
            "O=C(/C=C/c1ccc(O)c(O)c1)O[C@H]1[C@H](O)C[C@](O)(C(=O)O)C[C@H]1OC(=O)/C=C/c1ccc(O)c(O)c1\tCHEMBL177126\n",
            "O=C(O)C(=O)CC(=O)c1cccc(Cc2cccc(Cl)c2)c1\tCHEMBL145335\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#checking number of molecules in dataset\n",
        "! cat molecule.smi | wc -l"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6vBVWdpM6I-V",
        "outputId": "900e857d-c426-4754-be03-dd9caffaee2e"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4072\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#description of padel function\n",
        "! cat padel.sh"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fKaPfBaG6WrF",
        "outputId": "0942a3f8-67cc-4ff3-cc36-ece25cadbb57"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "java -Xms1G -Xmx1G -Djava.awt.headless=true -jar ./PaDEL-Descriptor/PaDEL-Descriptor.jar -removesalt -standardizenitro -fingerprints -descriptortypes ./PaDEL-Descriptor/PubchemFingerprinter.xml -dir ./ -file descriptors_output.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##calculating PaDEL descriptors\n",
        "#! bash padel.sh"
      ],
      "metadata": {
        "id": "FC59pm7i8aDx"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#taking a look at the PaDEL output\n",
        "df_3c_X = pd.read_csv(\"descriptors_output.csv\")\n",
        "df_3c_X "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "B4qGHmY-EQ7t",
        "outputId": "b07a073f-8832-4410-eb5a-0997bef3acbc"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CHEMBL145335</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CHEMBL177126</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4067</th>\n",
              "      <td>CHEMBL4649015</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4068</th>\n",
              "      <td>CHEMBL4640673</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4069</th>\n",
              "      <td>CHEMBL4633363</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4070</th>\n",
              "      <td>CHEMBL4644631</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "\n",
              "[4072 rows x 882 columns]"
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          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#removing redundant name column\n",
        "df_3c_X = df_3c_X.drop(columns=['Name'])\n",
        "df_3c_X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "I0m6l5v1GhJ7",
        "outputId": "bbd60447-82c4-4c71-c842-01d9a5712937"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4067</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4068</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <th>4069</th>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <th>4070</th>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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          "metadata": {},
          "execution_count": 50
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#concatenating the pIC50 column with PaDEL descriptors\n",
        "df3_Y = df_final[\"pIC50\"]\n",
        "dataset3 = pd.concat([df_3c_X,df3_Y], axis = 1)\n",
        "dataset3"
      ],
      "metadata": {
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        "id": "y9Nb5rb1GyCT",
        "outputId": "040f7f42-2977-4b08-8fd7-43d9761e99eb"
      },
      "execution_count": 51,
      "outputs": [
        {
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              "      <td>0</td>\n",
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              "      <td>5.769551</td>\n",
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              "    <tr>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>6.602060</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>7.000000</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>5.154902</td>\n",
              "    </tr>\n",
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              "      <td>5.004365</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4072 rows × 882 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c6407507-ec52-4a2a-baa8-d6ffba01428f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "      \n",
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              "    .colab-df-container {\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "  "
            ],
            "text/plain": [
              "      PubchemFP0  PubchemFP1  PubchemFP2  ...  PubchemFP879  PubchemFP880     pIC50\n",
              "0              1           1           1  ...             0             0  5.045757\n",
              "1              1           1           0  ...             0             0  5.853872\n",
              "2              1           1           1  ...             0             0  5.769551\n",
              "3              1           1           0  ...             0             0  6.602060\n",
              "4              1           1           1  ...             0             0  7.000000\n",
              "...          ...         ...         ...  ...           ...           ...       ...\n",
              "4067           1           1           1  ...             0             0  5.408935\n",
              "4068           1           1           1  ...             0             0  5.677781\n",
              "4069           1           1           1  ...             0             0  4.823909\n",
              "4070           1           1           1  ...             0             0  5.154902\n",
              "4071           1           1           1  ...             0             0  5.004365\n",
              "\n",
              "[4072 rows x 882 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#saving the prepared descriptors dataset\n",
        "dataset3.to_csv(\"HIV1_integrase_inhibitor_bioactivity_data_3class_pIC50_pubchem_fp_PaDEL.csv\", index=False)"
      ],
      "metadata": {
        "id": "bRHRZJVUIE3x"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Building"
      ],
      "metadata": {
        "id": "xzcTGzjzKyjV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#importing modelling libraries\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.feature_selection import VarianceThreshold"
      ],
      "metadata": {
        "id": "-1farrkgJDb1"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#rename the feature and label datasets \n",
        "X = df_3c_X\n",
        "Y = df3_Y"
      ],
      "metadata": {
        "id": "_UEqdsatL9G5"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#examining the data dimensions of the features dataframe\n",
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oTjaKhCcPE9T",
        "outputId": "9a79891d-4931-4604-8813-1c239a2f4e65"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4072, 881)"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#examining the data dimensions of the labels dataframe\n",
        "Y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "loCA0-7tPFlH",
        "outputId": "422ebdc9-ef4a-45f2-af90-0b8d2937fa74"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4072,)"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#removing low variance features\n",
        "selection = VarianceThreshold(threshold=(.8 * (1 - .8)))    \n",
        "X = selection.fit_transform(X)"
      ],
      "metadata": {
        "id": "VN67LqrPPFar"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#examining the data dimensions of the high variance features dataframe\n",
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hZeg9DTEPGPy",
        "outputId": "04192059-d8a8-4473-aca1-e1f94520a630"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4072, 184)"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#splitting the data into 80/20 proportion for training and testing respectively\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size= 0.2)"
      ],
      "metadata": {
        "id": "fNG6NVgJR3Jq"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#examining the data dimensions of the training datasets\n",
        "X_train.shape, Y_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Krm55JDFR3C2",
        "outputId": "59dfadce-25e5-4aa1-fd47-2ea7fc8b3922"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((3257, 184), (3257,))"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#examining the data dimensions of the testing datasets\n",
        "X_test.shape, Y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "96tLnBYWR28W",
        "outputId": "05c10c47-c81d-4b98-bdb7-3b9e099355dc"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((815, 184), (815,))"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#building a regression model using Random Forest\n",
        "RFR = RandomForestRegressor(n_estimators=100)\n",
        "RFR.fit(X_train, Y_train)\n",
        "r2 = RFR.score(X_test, Y_test)\n",
        "r2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6kx7UV8sSpyA",
        "outputId": "b3716e87-5159-4aa1-a7e7-a8d3a3da6599"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5676910437388634"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#scatter plot of predicted vs actual (experimental) pIC50 values\n",
        "Y_pred = RFR.predict(X_test)\n",
        "\n",
        "sns.set(color_codes=True)\n",
        "sns.set_style(\"white\")\n",
        "\n",
        "ax = sns.regplot(Y_test, Y_pred, scatter_kws={'alpha':0.4})\n",
        "ax.set_xlabel('Experimental pIC50', fontsize='large', fontweight='bold')\n",
        "ax.set_ylabel('Predicted pIC50', fontsize='large', fontweight='bold')\n",
        "ax.set_xlim(0, 11)\n",
        "ax.set_ylim(0, 11)\n",
        "ax.figure.set_size_inches(5, 5)\n",
        "plt.show"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "aiiNETScSpqj",
        "outputId": "3f150e3a-9e47-4890-ea3d-9e63b3f563be"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show>"
            ]
          },
          "metadata": {},
          "execution_count": 63
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Comparing performance of different regressors"
      ],
      "metadata": {
        "id": "D3qhqjViVrEn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#importing more regressors\n",
        "from sklearn.ensemble import RandomForestRegressor, ExtraTreesRegressor, GradientBoostingRegressor, AdaBoostRegressor\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.svm import SVR, LinearSVR\n",
        "from sklearn.neural_network import MLPRegressor\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from time import time\n",
        "from matplotlib.pyplot import subplots, axes, bar, xticks\n",
        "from random import seed\n",
        "\n",
        "RANDOM_STATE = 123 \n",
        "seed(RANDOM_STATE)\n",
        "\n",
        "classifiers = {\n",
        "'Random Forest': RandomForestRegressor(random_state=RANDOM_STATE),\n",
        "'AdaBoost (Random Forest)': AdaBoostRegressor(RandomForestRegressor(random_state=RANDOM_STATE)),\n",
        "'Extra Trees': ExtraTreesRegressor(random_state=RANDOM_STATE),\n",
        "'AdaBoost (Extra Tree)': AdaBoostRegressor(ExtraTreesRegressor(random_state=RANDOM_STATE)),\n",
        "'Decision Tree': DecisionTreeRegressor(random_state=RANDOM_STATE),\n",
        "'SVC (RBF)': SVR(),\n",
        "'SVC (Linear)': LinearSVR(random_state=RANDOM_STATE),\n",
        "'Multi-layer Perceptron': MLPRegressor(max_iter=5000,random_state=RANDOM_STATE)\n",
        "}\n"
      ],
      "metadata": {
        "id": "P7lv0M9PSpjP"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#scoring the selected regressors\n",
        "fig, all_axes = subplots(figsize=[15, 5], ncols=4, nrows=2, sharey=True, sharex=True)\n",
        "plt.subplots_adjust(wspace=0.7, hspace=0.5)\n",
        "\n",
        "#for-loop to train the models, make predictions on test data, plot predicted vs. experimental pIC50 values, calculate scores, and calculate time taken for computation.\n",
        "for ax, (name, clf) in zip(all_axes.ravel(), classifiers.items()):\n",
        "  start = time()\n",
        "  clf.fit(X_train, Y_train)\n",
        "  Y_pred = clf.predict(X_test)\n",
        "  y_ft_min, y_ft_max = Y_pred.min()*0.8, Y_pred.max()*1.2\n",
        "  score = clf.score(X_test, Y_test)\n",
        "  end = time()\n",
        "  time_taken = end-start\n",
        "  ax.scatter(Y_test, Y_pred)\n",
        "  ax.plot(np.unique(Y_test), np.poly1d(np.polyfit(Y_test, Y_pred, 1))(np.unique(Y_test)), color = \"k\")\n",
        "  label = '{} - R^2={:.2f}, t={:.2f}s'.format(name, score, time_taken)\n",
        "  ax.set_title(label , fontsize=10);\n",
        "  ax.set_xlabel(\"Experimental pIC50\") \n",
        "  ax.set_ylabel(\"Predicted pIC50\") \n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "rs5RimgYR2zP",
        "outputId": "c15d2a3c-a0a4-4284-8a42-2725a62fe15a"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/svm/_base.py:1208: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  ConvergenceWarning,\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 8 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Conclusions\n",
        "\n",
        "Out of the 8 models tested, Random Forest, AdaBoost (Random Forest), AdaBoost (Extra Tree), SVC (RBF), and Multi-layer Perception algorithms performed well, with R^2 value >= 0.5. Random forest had the highest score and the 2nd lowest time taken to compute out of the well-performing algorithms, making it arguably the most optimal model for this task as judged by these criteria. "
      ],
      "metadata": {
        "id": "VOm1mpGjwLRF"
      }
    }
  ]
}