| ---
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| language:
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| - en
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| license: gpl-3.0
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| tags:
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| - molecular-docking
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| - drug-discovery
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| - distributed-computing
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| - autodock
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| - boinc
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| - computational-chemistry
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| - bioinformatics
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| - gpu-acceleration
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| - distributed-network
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| - decentralized
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| datasets:
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| - protein-data-bank
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| - pubchem
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| - chembl
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| metrics:
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| - binding-energy
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| - rmsd
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| - computation-time
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| library_name: docking-at-home
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| pipeline_tag: boinc
|
| ---
|
|
|
| # Docking@HOME: Distributed Molecular Docking Platform
|
|
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| <div align="center">
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| <img src="https://via.placeholder.com/800x200/4A90E2/FFFFFF?text=Docking%40HOME" alt="Docking@HOME Banner">
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| </div>
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|
|
| ## Model Card Authors
|
|
|
| This model card is authored by:
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| - **OpenPeer AI** - AI/ML Integration & Cloud Agents Development
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| - **Riemann Computing Inc.** - Distributed Computing Architecture & System Design
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| - **Bleunomics** - Bioinformatics & Drug Discovery Expertise
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| - **Andrew Magdy Kamal** - Project Lead & System Integration
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|
|
| ## Model Overview
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|
|
| Docking@HOME is a state-of-the-art distributed computing platform for molecular docking simulations that combines multiple cutting-edge technologies to democratize computational drug discovery. The platform leverages volunteer computing (BOINC), GPU acceleration (CUDPP), decentralized networking (Distributed Network Settings), and AI-driven orchestration (Cloud Agents) to enable large-scale molecular docking at unprecedented speeds.
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|
|
| ### Key Features
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|
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| - 🧬 **AutoDock Integration**: Industry-standard molecular docking engine (v4.2.6)
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| - 🚀 **GPU Acceleration**: CUDA/CUDPP-powered parallel processing
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| - 🌐 **Distributed Computing**: BOINC framework for global volunteer computing
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| - 🔗 **Decentralized Coordination**: Distributed Network Settings-based task distribution
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| - 🤖 **AI Orchestration**: Cloud Agents for intelligent resource allocation
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| - 📊 **Scalable**: From single workstation to thousands of nodes
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| - 🔒 **Transparent**: All computations recorded on distributed network
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| - 🆓 **Open Source**: GPL-3.0 licensed
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|
|
| ## Architecture
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|
|
| Docking@HOME employs a multi-layered architecture:
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|
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| 1. **Task Submission Layer**: Users submit docking jobs via CLI, API, or web interface
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| 2. **AI Orchestration Layer**: Cloud Agents optimize task distribution
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| 3. **Decentralized Coordination Layer**: Distributed Network Settings ensure transparent task allocation
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| 4. **Distribution Layer**: BOINC manages volunteer computing resources
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| 5. **Computation Layer**: AutoDock performs docking with GPU acceleration
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| 6. **Results Aggregation Layer**: Collect, validate, and store results
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|
|
| ## Intended Use
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|
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| ### Primary Use Cases
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|
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| - **Drug Discovery**: Virtual screening of compound libraries against protein targets
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| - **Academic Research**: Computational chemistry and structural biology studies
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| - **Pandemic Response**: Rapid screening for therapeutic candidates
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| - **Educational**: Teaching molecular docking and distributed computing concepts
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| - **Benchmark**: Testing distributed computing frameworks and GPU performance
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|
|
| ### Out-of-Scope Use Cases
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|
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| - Clinical diagnosis or treatment recommendations
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| - Production pharmaceutical manufacturing decisions without expert validation
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| - Real-time emergency medical applications
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| - Replacement for experimental validation
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|
|
| ## Technical Specifications
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|
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| ### Input Format
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|
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| - **Ligands**: PDBQT format (prepared small molecules)
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| - **Receptors**: PDBQT format (prepared protein structures)
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| - **Parameters**: JSON configuration files
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|
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| ### Output Format
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|
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| - **Binding Poses**: PDBQT format with 3D coordinates
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| - **Energies**: Binding energy (kcal/mol), intermolecular, internal, torsional
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| - **Ranking**: Clustered by RMSD with energy-based ranking
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| - **Metadata**: Computation time, node info, validation hash
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|
|
| ### Performance Metrics
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|
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| #### Benchmark Results (RTX 3090 GPU)
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|
|
| | Metric | Value |
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| |--------|-------|
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| | Docking Runs per Hour | ~2,000 |
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| | Average Time per Run | ~1.8 seconds |
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| | GPU Speedup vs CPU | ~20x |
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| | Memory Usage | ~4GB GPU RAM |
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| | Power Efficiency | ~100 runs/kWh |
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|
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| #### Distributed Performance (1000 nodes)
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|
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| | Metric | Value |
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| |--------|-------|
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| | Total Throughput | 100,000+ runs/hour |
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| | Task Overhead | <5% |
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| | Network Latency | <100ms average |
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| | Fault Tolerance | 99.9% uptime |
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|
|
| ## Training Details
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|
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| This is not a traditional machine learning model but a computational platform. The platform uses:
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|
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| - **AutoDock**: Physics-based scoring function (empirically parameterized)
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| - **Genetic Algorithm**: For conformational search
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| - **Cloud Agents**: Pre-trained AI models for resource optimization
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|
|
| ## Validation & Testing
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|
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| ### Validation Protocol
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|
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| 1. **Redocking Tests**: Reproduce known crystal structure binding poses (RMSD < 2Å)
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| 2. **Cross-Docking**: Test on different conformations of same protein
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| 3. **Enrichment Tests**: Ability to identify known binders from decoys
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| 4. **Benchmark Sets**: Validated against CASF, DUD-E, and other standard sets
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|
|
| ### Success Criteria
|
|
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| - **RMSD < 2.0 Å**: 85% success rate on redocking tests
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| - **Energy Correlation**: R² > 0.7 with experimental binding affinities
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| - **Enrichment Factor**: >10 for known actives vs decoys
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| - **Reproducibility**: 99.9% identical results across multiple runs
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|
|
| ## Limitations & Biases
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|
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| ### Known Limitations
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|
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| 1. **Flexibility**: Limited receptor flexibility (rigid docking primarily)
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| 2. **Solvation**: Simplified water models may miss key interactions
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| 3. **Metals**: Limited handling of metal coordination
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| 4. **Entropy**: Approximated entropy calculations
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| 5. **Post-Dock**: Requires expert analysis and experimental validation
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|
|
| ### Potential Biases
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|
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| 1. **Parameter Bias**: Scoring function optimized on specific protein families
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| 2. **Dataset Bias**: Training on predominantly drug-like molecules
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| 3. **Structural Bias**: Better performance on well-defined binding pockets
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| 4. **Resource Bias**: GPU access required for optimal performance
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|
|
| ### Mitigation Strategies
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|
|
| - Provide multiple scoring functions
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| - Support custom parameter sets
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| - Enable CPU-only mode for accessibility
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| - Comprehensive documentation on limitations
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| - Encourage ensemble docking approaches
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|
|
| ## Ethical Considerations
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|
|
| ### Responsible Use
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|
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| - **Open Science**: All results timestamped on distributed network for reproducibility
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| - **Attribution**: Volunteer contributors credited in publications
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| - **Data Privacy**: No personal data collected from volunteers
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| - **Environmental**: GPU efficiency optimizations reduce carbon footprint
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| - **Accessibility**: Free for academic and non-profit research
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|
|
| ### Potential Risks
|
|
|
| - **Dual Use**: Could be used for harmful compound design (mitigated by access controls)
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| - **Over-reliance**: Results must be validated experimentally
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| - **Resource Inequality**: GPU requirements may limit access (mitigated by distributed model)
|
|
|
| ## Carbon Footprint
|
|
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| ### Estimated CO₂ Emissions
|
|
|
| - **Single GPU (24h operation)**: ~5 kg CO₂
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| - **Distributed Network (1000 nodes, 1 year)**: ~43,800 kg CO₂
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| - **Offset Programs**: Partner with carbon offset initiatives
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| - **Efficiency**: 20x more efficient than CPU-only approaches
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|
|
| ## Getting Started
|
|
|
| ### Installation
|
|
|
| ```bash
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| # Clone repository
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| git clone https://huggingface.co/OpenPeerAI/DockingAtHOME
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| cd DockingAtHOME
|
|
|
| # Install dependencies
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| pip install -r requirements.txt
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| npm install
|
|
|
| # Build C++/CUDA components
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| mkdir build && cd build
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| cmake .. && make -j$(nproc)
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| ```
|
|
|
| ### Quick Start with GUI
|
|
|
| ```bash
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| # Start the web-based GUI (fastest way to get started)
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| docking-at-home gui
|
|
|
| # Or with Python
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| python -m docking_at_home.gui
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|
|
| # Open browser to http://localhost:8080
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| ```
|
|
|
| ### Quick Start Example (CLI)
|
|
|
| ```python
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| from docking_at_home import DockingClient
|
|
|
| # Initialize client (localhost mode)
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| client = DockingClient(mode="localhost")
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|
|
| # Submit docking job
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| job = client.submit_job(
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| ligand="path/to/ligand.pdbqt",
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| receptor="path/to/receptor.pdbqt",
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| num_runs=100
|
| )
|
|
|
| # Monitor progress
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| status = client.get_status(job.id)
|
|
|
| # Retrieve results
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| results = client.get_results(job.id)
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| print(f"Best binding energy: {results.best_energy} kcal/mol")
|
| ```
|
|
|
| ### Running on Localhost
|
|
|
| ```bash
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| # Start server
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| docking-at-home server --port 8080
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|
|
| # In another terminal, run worker
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| docking-at-home worker --local
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| ```
|
|
|
| ## Citation
|
|
|
| ```bibtex
|
| @software{docking_at_home_2025,
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| title={Docking@HOME: A Distributed Platform for Molecular Docking},
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| author={OpenPeer AI and Riemann Computing Inc. and Bleunomics and Andrew Magdy Kamal},
|
| year={2025},
|
| url={https://huggingface.co/OpenPeerAI/DockingAtHOME},
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| license={GPL-3.0}
|
| }
|
| ```
|
|
|
| ### Component Citations
|
|
|
| Please also cite the underlying technologies:
|
|
|
| ```bibtex
|
| @article{morris2009autodock4,
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| title={AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility},
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| author={Morris, Garrett M and Huey, Ruth and Lindstrom, William and Sanner, Michel F and Belew, Richard K and Goodsell, David S and Olson, Arthur J},
|
| journal={Journal of computational chemistry},
|
| volume={30},
|
| number={16},
|
| pages={2785--2791},
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| year={2009}
|
| }
|
|
|
| @article{anderson2004boinc,
|
| title={BOINC: A system for public-resource computing and storage},
|
| author={Anderson, David P},
|
| journal={Grid Computing, 2004. Proceedings. Fifth IEEE/ACM International Workshop on},
|
| pages={4--10},
|
| year={2004},
|
| organization={IEEE}
|
| }
|
| ```
|
|
|
| ## Community & Support
|
|
|
| - **HuggingFace**: [huggingface.co/OpenPeerAI/DockingAtHOME](https://huggingface.co/OpenPeerAI/DockingAtHOME)
|
| - **Issues & Discussions**: [HuggingFace Discussions](https://huggingface.co/OpenPeerAI/DockingAtHOME/discussions)
|
| - **Email**: andrew@bleunomics.com
|
|
|
| ## Contributing
|
|
|
| We welcome contributions from the community! Please see [CONTRIBUTING.md](https://huggingface.co/OpenPeerAI/DockingAtHOME/blob/main/CONTRIBUTING.md)
|
|
|
| ### Areas for Contribution
|
|
|
| - Algorithm improvements
|
| - GPU optimization
|
| - Web interface development
|
| - Documentation
|
| - Testing
|
| - Bug reports
|
| - Use case examples
|
|
|
| ## License
|
|
|
| This project is licensed under the GNU General Public License v3.0 - see [LICENSE](LICENSE) for details.
|
|
|
| Individual components retain their original licenses:
|
| - **AutoDock**: GNU GPL v2
|
| - **BOINC**: GNU LGPL v3
|
| - **CUDPP**: BSD License
|
| - **Decentralized Internet SDK**: Various open-source licenses
|
|
|
| ## Acknowledgments
|
|
|
| - The AutoDock development team at The Scripps Research Institute
|
| - UC Berkeley's BOINC project
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| - CUDPP developers and NVIDIA
|
| - Lonero Team for the Decentralized Internet SDK
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| - OpenPeer AI for Cloud Agents framework
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| - All volunteer computing contributors worldwide
|
|
|
| ## Version History
|
|
|
| ### v1.0.0 (2025)
|
|
|
| - Initial release
|
| - AutoDock 4.2.6 integration
|
| - BOINC distributed computing support
|
| - CUDA/CUDPP GPU acceleration
|
| - Decentralized Internet SDK integration
|
| - Cloud Agents AI orchestration
|
| - HuggingFace model card and datasets
|
|
|
| ---
|
|
|
| **Built with ❤️ by the open-source computational chemistry community**
|
|
|
| *Repository: https://huggingface.co/OpenPeerAI/DockingAtHOME*
|
| *Support: andrew@bleunomics.com*
|
|
|
