RepurposAI - Drug & Target Discovery Studio

RepurposAI is a modular, open-source web app for drug repurposing and target discovery.
It integrates ML, cheminformatics, and the Open Targets Platform.

Getting Started

1. Install dependencies: pip install -r requirements.txt
2. Run the Streamlit app: streamlit run app/main.py
3. Explore modules: similarity search, target prediction, pathway mapping

Here’s the project structure:

RepurposAI/
├── README.md
├── requirements.txt
├── .gitignore
├── app/
│   ├── main.py                 # Streamlit main app
│   ├── dashboard.py            # Dashboard assembly module
│   ├── visualization.py        # Plotly/Seaborn visualization module
│   └── utils/
│       ├── api_integration.py  # Open Targets & KEGG API helpers
│       ├── similarity.py       # RDKit similarity functions
│       └── ml_model.py         # Placeholder for target prediction ML
├── data/                       # Sample data or placeholder CSVs
├── docs/                       # README, usage guide, hackathon slides
├── tests/                      # Basic test scripts for functions
└── notebooks/                  # Jupyter notebooks for testing ML/API modules
    └── example_notebook.ipynb

✅ Next Steps for Hackathon Participants

• Fork the repository and pick a task from the GitHub Epic.
• Implement the placeholder modules in /app and /app/utils.
• Add sample Jupyter notebooks in /notebooks to test modules.
• Start building Streamlit pages in /app/main.py and /app/dashboard.py.
• Commit your work regularly and create issues/sub-issues for new features or bugs.
• Tag issues with relevant labels: backend, frontend, api, ml, docs, streamlit, rdkit.