An AI-driven system for managing, categorizing, and securely storing docking simulation data using Solana blockchain and decentralized storage.

Efficiently organize and manage docking simulation data (from commercial & open-source tools) to accelerate research in small-molecule inhibitors for target proteins.

• Protein Targets: Group by targeted proteins.
• Binding Efficacy: Categorize by affinity scores.
• Molecular Properties: Identify physicochemical & ADMET properties.

• Immutable Records: Smart contracts ensure data integrity.
• Decentralized Storage: IPFS/Arweave for files + Solana references.
• Full Auditability: Transparent data provenance.

• Search & Filter: By protein, efficacy, or molecular traits.
• Interactive Visualizations:
  • Docking score distributions.
  • Molecular interaction heatmaps.
  • 3D docking previews.
• Export/Integration: Seamless ML model integration.

1. Data Ingestion: Upload docking results → AI categorization.
2. Blockchain Recording:
   • Metadata hashed on Solana.
   • Files stored on IPFS/Arweave.
3. User Access: Intuitive UI + real-time smart contract queries.

1. Clone the repository:

   git clone https://github.com/e-man07/DockMind
   cd DockMind

2. Create and activate a virtual environment (Terminal 1):

   cd backend
   python -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Run the backend server:

   python src/run_api.py

5. Setup the frontend (Terminal 2):

   cd frontend
   npm install
   npm run dev

6. Access the application:

   Open your browser and navigate to http://localhost:3000.