MODEL_CARD.md

---

language:
- en
license: gpl-3.0
tags:
- molecular-docking
- drug-discovery
- distributed-computing
- autodock
- boinc
- computational-chemistry
- bioinformatics
- gpu-acceleration
- distributed-network
- decentralized
datasets:
- protein-data-bank
- pubchem
- chembl
metrics:
- binding-energy
- rmsd
- computation-time
library_name: docking-at-home
pipeline_tag: boinc
---


# Docking@HOME: Distributed Molecular Docking Platform

<div align="center">
  <img src="https://via.placeholder.com/800x200/4A90E2/FFFFFF?text=Docking%40HOME" alt="Docking@HOME Banner">
</div>

## Model Card Authors

This model card is authored by:
- **OpenPeer AI** - AI/ML Integration & Cloud Agents Development
- **Riemann Computing Inc.** - Distributed Computing Architecture & System Design
- **Bleunomics** - Bioinformatics & Drug Discovery Expertise
- **Andrew Magdy Kamal** - Project Lead & System Integration

## Model Overview

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.

### Key Features

- 🧬 **AutoDock Integration**: Industry-standard molecular docking engine (v4.2.6)
- 🚀 **GPU Acceleration**: CUDA/CUDPP-powered parallel processing
- 🌐 **Distributed Computing**: BOINC framework for global volunteer computing
- 🔗 **Decentralized Coordination**: Distributed Network Settings-based task distribution
- 🤖 **AI Orchestration**: Cloud Agents for intelligent resource allocation
- 📊 **Scalable**: From single workstation to thousands of nodes
- 🔒 **Transparent**: All computations recorded on distributed network
- 🆓 **Open Source**: GPL-3.0 licensed

## Architecture

Docking@HOME employs a multi-layered architecture:

1. **Task Submission Layer**: Users submit docking jobs via CLI, API, or web interface
2. **AI Orchestration Layer**: Cloud Agents optimize task distribution
3. **Decentralized Coordination Layer**: Distributed Network Settings ensure transparent task allocation
4. **Distribution Layer**: BOINC manages volunteer computing resources
5. **Computation Layer**: AutoDock performs docking with GPU acceleration
6. **Results Aggregation Layer**: Collect, validate, and store results

## Intended Use

### Primary Use Cases

- **Drug Discovery**: Virtual screening of compound libraries against protein targets
- **Academic Research**: Computational chemistry and structural biology studies
- **Pandemic Response**: Rapid screening for therapeutic candidates
- **Educational**: Teaching molecular docking and distributed computing concepts
- **Benchmark**: Testing distributed computing frameworks and GPU performance

### Out-of-Scope Use Cases

- Clinical diagnosis or treatment recommendations
- Production pharmaceutical manufacturing decisions without expert validation
- Real-time emergency medical applications
- Replacement for experimental validation

## Technical Specifications

### Input Format

- **Ligands**: PDBQT format (prepared small molecules)
- **Receptors**: PDBQT format (prepared protein structures)
- **Parameters**: JSON configuration files

### Output Format

- **Binding Poses**: PDBQT format with 3D coordinates
- **Energies**: Binding energy (kcal/mol), intermolecular, internal, torsional
- **Ranking**: Clustered by RMSD with energy-based ranking
- **Metadata**: Computation time, node info, validation hash

### Performance Metrics

#### Benchmark Results (RTX 3090 GPU)

| Metric | Value |
|--------|-------|
| Docking Runs per Hour | ~2,000 |
| Average Time per Run | ~1.8 seconds |
| GPU Speedup vs CPU | ~20x |
| Memory Usage | ~4GB GPU RAM |
| Power Efficiency | ~100 runs/kWh |

#### Distributed Performance (1000 nodes)

| Metric | Value |
|--------|-------|
| Total Throughput | 100,000+ runs/hour |
| Task Overhead | <5% |
| Network Latency | <100ms average |
| Fault Tolerance | 99.9% uptime |

## Training Details

This is not a traditional machine learning model but a computational platform. The platform uses:

- **AutoDock**: Physics-based scoring function (empirically parameterized)
- **Genetic Algorithm**: For conformational search
- **Cloud Agents**: Pre-trained AI models for resource optimization

## Validation & Testing

### Validation Protocol

1. **Redocking Tests**: Reproduce known crystal structure binding poses (RMSD < 2Å)
2. **Cross-Docking**: Test on different conformations of same protein
3. **Enrichment Tests**: Ability to identify known binders from decoys
4. **Benchmark Sets**: Validated against CASF, DUD-E, and other standard sets

### Success Criteria

- **RMSD < 2.0 Å**: 85% success rate on redocking tests
- **Energy Correlation**: R² > 0.7 with experimental binding affinities
- **Enrichment Factor**: >10 for known actives vs decoys
- **Reproducibility**: 99.9% identical results across multiple runs

## Limitations & Biases

### Known Limitations

1. **Flexibility**: Limited receptor flexibility (rigid docking primarily)
2. **Solvation**: Simplified water models may miss key interactions
3. **Metals**: Limited handling of metal coordination
4. **Entropy**: Approximated entropy calculations
5. **Post-Dock**: Requires expert analysis and experimental validation

### Potential Biases

1. **Parameter Bias**: Scoring function optimized on specific protein families
2. **Dataset Bias**: Training on predominantly drug-like molecules
3. **Structural Bias**: Better performance on well-defined binding pockets
4. **Resource Bias**: GPU access required for optimal performance

### Mitigation Strategies

- Provide multiple scoring functions
- Support custom parameter sets
- Enable CPU-only mode for accessibility
- Comprehensive documentation on limitations
- Encourage ensemble docking approaches

## Ethical Considerations

### Responsible Use

- **Open Science**: All results timestamped on distributed network for reproducibility
- **Attribution**: Volunteer contributors credited in publications
- **Data Privacy**: No personal data collected from volunteers
- **Environmental**: GPU efficiency optimizations reduce carbon footprint
- **Accessibility**: Free for academic and non-profit research

### Potential Risks

- **Dual Use**: Could be used for harmful compound design (mitigated by access controls)
- **Over-reliance**: Results must be validated experimentally
- **Resource Inequality**: GPU requirements may limit access (mitigated by distributed model)

## Carbon Footprint

### Estimated CO₂ Emissions

- **Single GPU (24h operation)**: ~5 kg CO₂
- **Distributed Network (1000 nodes, 1 year)**: ~43,800 kg CO₂
- **Offset Programs**: Partner with carbon offset initiatives
- **Efficiency**: 20x more efficient than CPU-only approaches

## Getting Started

### Installation

```bash

# Clone repository

git clone https://huggingface.co/OpenPeerAI/DockingAtHOME

cd DockingAtHOME



# Install dependencies

pip install -r requirements.txt

npm install



# Build C++/CUDA components

mkdir build && cd build

cmake .. && make -j$(nproc)

```

### Quick Start with GUI

```bash

# Start the web-based GUI (fastest way to get started)

docking-at-home gui



# Or with Python

python -m docking_at_home.gui



# Open browser to http://localhost:8080

```

### Quick Start Example (CLI)

```python

from docking_at_home import DockingClient



# Initialize client (localhost mode)

client = DockingClient(mode="localhost")



# Submit docking job

job = client.submit_job(

    ligand="path/to/ligand.pdbqt",

    receptor="path/to/receptor.pdbqt",

    num_runs=100

)



# Monitor progress

status = client.get_status(job.id)



# Retrieve results

results = client.get_results(job.id)

print(f"Best binding energy: {results.best_energy} kcal/mol")

```

### Running on Localhost

```bash

# Start server

docking-at-home server --port 8080



# In another terminal, run worker

docking-at-home worker --local

```

## Citation

```bibtex

@software{docking_at_home_2025,

  title={Docking@HOME: A Distributed Platform for Molecular Docking},

  author={OpenPeer AI and Riemann Computing Inc. and Bleunomics and Andrew Magdy Kamal},

  year={2025},

  url={https://huggingface.co/OpenPeerAI/DockingAtHOME},

  license={GPL-3.0}

}

```

### Component Citations

Please also cite the underlying technologies:

```bibtex

@article{morris2009autodock4,

  title={AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility},

  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},

  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
- CUDPP developers and NVIDIA
- Lonero Team for the Decentralized Internet SDK
- OpenPeer AI for Cloud Agents framework
- 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*