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Drug Discovery Pipeline for SARS Coronavirus

Overview

This project presents a comprehensive pipeline for drug discovery targeting the SARS coronavirus. It utilizes data from the ChEMBL database, employs various data preprocessing techniques, performs analysis, and builds a predictive model to identify potential drug candidates.

Features

  • Data retrieval from ChEMBL database
  • Data preprocessing including cleaning and filtering
  • Calculation of bioactivity thresholds
  • Calculation of Lipinski descriptors
  • Visualization of data through plots
  • Statistical analysis using Mann-Whitney U test
  • PubChem fingerprinting for feature generation
  • Model building using Random Forest regression
  • Model evaluation using R-squared and mean squared error
  • Exporting trained model for future use

Installation

  1. Clone the repository:
git clone https://github.com/itsNileshHere/Drug-Discovery.git
cd Drug-Discovery

Usage

  1. Run the Jupyter notebook Drug Detection.ipynb to execute the entire pipeline.
  2. Follow the step-by-step instructions within the notebook to preprocess data, analyze, build, and evaluate the model.
  3. Modify the notebook as needed for custom datasets or analyses.

Data

  • The data used in this project is retrieved from the ChEMBL database.
  • PubChem fingerprints are generated for feature extraction using the PaDEL software.