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.