| ---
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| title: GSS DiffDock Engine
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| emoji: 🧬
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| colorFrom: purple
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| colorTo: pink
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| sdk: gradio
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| sdk_version: "4.36.1"
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| python_version: "3.10"
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| app_file: app.py
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| pinned: false
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| ---
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|
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| # DiffDock API Layer for Window 8 Drug Development
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|
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| ## Overview
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| This directory contains the optimized DiffDock molecular docking engine designed to run on Hugging Face's **free CPU Basic tier** (2 vCPUs). It provides protein-ligand binding affinity predictions for drug development analysis.
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|
|
| ## Architecture
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| - **Platform**: Hugging Face Spaces (Gradio SDK)
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| - **Hardware**: CPU Basic (Free Tier - 2 vCPUs)
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| - **Framework**: DiffDock neural network for molecular docking
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| - **API**: RESTful endpoint for Cloudflare Worker integration
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|
|
| ## Files
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| ### 1. `packages.txt`
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| System-level dependencies installed before Python setup:
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| - `unzip` - Archive extraction
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| - `wget` - File downloads
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| - `libgl1-mesa-glx` - OpenGL support for molecular visualization
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|
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| ### 2. `requirements.txt`
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| Python dependencies optimized for CPU execution:
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| - **PyTorch 2.2.1** (CPU-only build)
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| - **torch-geometric 2.5.2** - Graph neural networks
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| - **biopython 1.83** - Biological computation
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| - **rdkit 2023.9.5** - Chemical informatics
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| - **gradio 4.19.2** - Web interface and API
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| - **pandas 2.2.1** - Data manipulation
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| - **pyyaml 6.0.1** - Configuration parsing
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| - **scipy 1.12.0** - Scientific computing
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| - **networkx 3.2.1** - Graph algorithms
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|
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| ### 3. `app.py`
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| Main application with three key components:
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|
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| #### CPU Optimization
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| ```python
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| torch.set_num_threads(2)
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| os.environ["OMP_NUM_THREADS"] = "2"
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| os.environ["MKL_NUM_THREADS"] = "2"
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| ```
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| Limits thread usage to match free tier allocation.
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|
|
| #### Automated Setup
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| - Clones DiffDock repository
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| - Downloads pre-trained weights from Zenodo
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| - Configures inference pipeline
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|
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| #### API Endpoint
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| - **Function**: `run_diffdock_inference(protein_pdb_content, ligand_smiles_string)`
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| - **Input**:
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| - Protein structure (PDB format)
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| - Ligand molecule (SMILES string)
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| - **Output**: JSON with confidence score
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| - **API Name**: `execute_diffdock_prediction`
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|
|
| ## Deployment Steps
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|
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| ### 1. Create Hugging Face Space
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| 1. Go to https://huggingface.co/spaces
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| 2. Click **"Create a New Space"**
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| 3. Name: `gss-diffdock-engine` (or your preferred name)
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| 4. SDK: **Gradio**
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| 5. Hardware: **CPU Basic** (Free)
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| 6. Visibility: Public or Private
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|
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| ### 2. Upload Files
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| Upload these three files to your Space repository:
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| - `packages.txt`
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| - `requirements.txt`
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| - `app.py`
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|
|
| ### 3. Wait for Build
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| Hugging Face will:
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| 1. Install system packages (1-2 minutes)
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| 2. Install Python dependencies (3-5 minutes)
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| 3. Clone DiffDock and download weights (5-10 minutes)
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| 4. Start the application
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| Total build time: **10-15 minutes**
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|
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| ### 4. Verify Deployment
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| Once status shows **"Running"**:
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| - The Space URL will be active
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| - API endpoint will be available at: `https://YOUR-USERNAME-gss-diffdock-engine.hf.space/api/execute_diffdock_prediction`
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|
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| ## API Usage
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|
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| ### Request Format
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| ```bash
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| curl -X POST "https://YOUR-USERNAME-gss-diffdock-engine.hf.space/api/execute_diffdock_prediction" \
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| -H "Content-Type: application/json" \
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| -d '{
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| "data": [
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| "PROTEIN_PDB_CONTENT_HERE",
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| "LIGAND_SMILES_STRING_HERE"
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| ]
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| }'
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| ```
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|
|
| ### Response Format
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| ```json
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| {
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| "data": [{
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| "success": true,
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| "diffdock_confidence_score": 0.85,
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| "hardware_allocation": "HF_FREE_CPU_TIER"
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| }]
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| }
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| ```
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|
|
| ## Performance Optimizations
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|
|
| ### Memory Management
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| - **Inference steps**: Limited to 10 (vs default 20)
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| - **Samples per complex**: 1 (vs default 40)
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| - **Cleanup**: Automatic removal of temporary files
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|
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| ### CPU Constraints
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| - Thread count capped at 2
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| - Single pose generation
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| - Aggressive memory cleanup
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|
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| ## Integration with Cloudflare Worker
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|
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| The next step is to create a Cloudflare Worker handler that:
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| 1. Receives drug development requests from Window 8
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| 2. Formats protein/ligand data
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| 3. Calls this Hugging Face API
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| 4. Stores results in D1 database
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| 5. Returns predictions to frontend
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|
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| ## Troubleshooting
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|
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| ### Build Failures
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| - Check logs for missing dependencies
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| - Verify file names are exact (case-sensitive)
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| - Ensure no extra whitespace in files
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|
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| ### Timeout Errors
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| - Inference is limited to 10 steps for speed
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| - Consider upgrading to paid tier for faster processing
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|
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| ### Memory Issues
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| - Current config optimized for 16GB RAM limit
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| - Reduce inference steps if needed
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|
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| ## Next Steps
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| 1. ✅ Deploy to Hugging Face Spaces
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| 2. ⏳ Create Cloudflare Worker integration
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| 3. ⏳ Add D1 database schema for drug predictions
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| 4. ⏳ Build Window 8 frontend interface
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| 5. ⏳ Implement result visualization
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|
|
| ## Support
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|
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| For issues or questions:
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| - Hugging Face Docs: https://huggingface.co/docs/hub/spaces
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| - DiffDock Paper: https://arxiv.org/abs/2210.01776
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| - DiffDock Repo: https://github.com/gcorso/DiffDock
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|
|
| ---
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|
|
| **Gaston Software Solutions LLP**
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| Window 8: Drug Development & Molecular Docking Engine |
