Metadata-Version: 2.4
Name: repurposcan
Version: 0.1.1
Summary: Drug-target binding affinity prediction using XGBoost
Author: Ayush Laware
Description-Content-Type: text/markdown
Requires-Dist: numpy<2
Requires-Dist: pandas
Requires-Dist: scikit-learn
Requires-Dist: xgboost
Requires-Dist: rdkit-pypi
Requires-Dist: joblib
Dynamic: author
Dynamic: description
Dynamic: description-content-type
Dynamic: requires-dist
Dynamic: summary

# RepurpoScan

RepurpoScan is a machine learning-based Python package for predicting drug–target binding affinity (Kd) using molecular and protein sequence information. The package is designed to support drug repurposing by identifying potential interactions between existing compounds and new protein targets.

## Overview

Drug repurposing is an important strategy in modern drug discovery, enabling faster identification of therapeutic candidates by reusing existing drugs. RepurpoScan provides a simple interface to estimate binding affinity between a ligand and a protein using trained machine learning models.

## Features

- Predict binding affinity (Kd) from:
  - SMILES string (ligand representation)
  - Protein sequence
  - Protein type (kinase or receptor)
- Separate models trained for different protein classes
- Lightweight and easy-to-use API
- Built using XGBoost and RDKit

## Installation

Install the package using pip:

```bash
pip install repurposcan
