README.md

---
language:
- en
license: gpl-3.0
tags:
- molecular-docking
- drug-discovery
- distributed-computing
- autodock
- boinc
- chemistry
- biology
- agent
- computational-chemistry
- bioinformatics
- gpu-acceleration
- distributed-network
- decentralized
datasets:
- protein-data-bank
- pubchem
- chembl
metrics:
- binding-energy
- rmsd
- bleu
- accuracy
base_model:
- OpenPeerAI/Cloud-Agents
library_name: docking-at-home
pipeline_tag: other
---

# Docking@HOME

**Distributed and Parallel Molecular Docking Platform**

[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)

## Overview

Docking@HOME is a cutting-edge distributed computing platform that leverages the power of volunteer computing, GPU acceleration, decentralized networking, and AI-driven orchestration to perform large-scale molecular docking simulations. This project combines multiple state-of-the-art technologies to democratize drug discovery and computational chemistry.

### Key Features

- 🧬 **AutoDock Integration**: Uses AutoDock Suite 4.2.6 for molecular docking simulations
- 🚀 **GPU Acceleration**: CUDPP-powered parallel processing for enhanced performance
- 🌐 **Distributed Computing**: BOINC framework for volunteer computing at scale
- 🔗 **Decentralized Networking**: Distributed Network Settings-based coordination using the Decentralized Internet SDK
- 🤖 **AI Orchestration**: Cloud Agents for intelligent task distribution and optimization
- 📊 **HuggingFace Integration**: Model cards and datasets for reproducible research

## Architecture

```
           Docking@HOME Platform

           ┌─────────────────────┐
           │   BOINC Server    │
           │   (Task Mgmt)     │
           └─────────▲───────────┘
                      │
                      │
           ┌──────────┴───────────┐
           │  Decentralized     │
           │     Internet       │
           └─────────▲───────────┘
                      │
                      │
           ┌──────────┴───────────┐
           │   Cloud Agents     │
           │   (AI Routing)     │
           └──────────┬───────────┘
                      │
                      ▼
    ┌──────────────────────────────────────────────┐
    │      Distributed Worker Nodes (Clients)  │
    │ ┌────────────────┐      ┌────────────────┐  │
    │ │   AutoDock    │<---->│    CUDPP     │  │
    │ │  (Docking)    │      │ (GPU Accel)  │  │
    │ └────────────────┘      └────────────────┘  │
    └──────────────────────────────────────────────┘


```

## Components

### 1. AutoDock Suite (v4.2.6)
Core molecular docking engine that predicts binding modes and affinities of small molecules to protein targets.

### 2. CUDPP (CUDA Data Parallel Primitives Library)
Provides GPU-accelerated parallel primitives for enhancing AutoDock's computational performance.

### 3. BOINC (Berkeley Open Infrastructure for Network Computing)
Distributed computing middleware that manages volunteer computing resources globally.

### 4. The Decentralized Internet SDK
Enables Distributed Network Settings-based coordination, ensuring transparency and decentralization of task distribution.

### 5. Cloud Agents
AI-powered orchestration layer that optimizes task scheduling and resource allocation based on workload characteristics.

## Authors & Contributors

- **OpenPeer AI** - AI/ML Integration & Cloud Agents
- **Riemann Computing Inc.** - Distributed Computing Architecture
- **Bleunomics** - Bioinformatics & Drug Discovery Expertise
- **Andrew Magdy Kamal** - Project Lead & System Integration

## Installation

### Prerequisites

- C++ compiler (GCC 7+ or MSVC 2019+)
- CUDA Toolkit 11.0+ (for GPU acceleration)
- Python 3.8+
- Node.js 16+ (for the Decentralized Internet SDK)
- BOINC client/server software

### Build Instructions

```bash
# Clone the repository
git clone https://huggingface.co/OpenPeerAI/DockingAtHOME
cd DockingAtHOME

# Initialize submodules
git submodule update --init --recursive

# Build the project
mkdir build && cd build
cmake ..
make -j$(nproc)

# Install
sudo make install
```

## 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
```

### Python API

```python
from docking_at_home.server import job_manager, initialize_server
import asyncio

async def main():
    await initialize_server()
    
    job_id = await job_manager.submit_job(
        ligand_file="molecule.pdbqt",
        receptor_file="protein.pdbqt",
        num_runs=100,
        use_gpu=True
    )
    
    # Monitor progress
    while True:
        job = job_manager.get_job(job_id)
        if job["status"] == "completed":
            print(f"Best energy: {job['results']['best_energy']}")
            break
        await asyncio.sleep(1)

asyncio.run(main())
```

## Configuration

Configuration files are located in `config/`:

- `autodock.conf` - AutoDock parameters
- `boinc_server.conf` - BOINC server settings
- `gpu_config.conf` - CUDPP and GPU settings
- `decentralized.conf` - Distributed Network Settings
- `cloud_agents.conf` - AI orchestration parameters

## Performance

On a typical configuration:
- **CPU-only**: ~100 docking runs/hour
- **Single GPU (RTX 3090)**: ~2,000 docking runs/hour
- **Distributed (1000 nodes)**: ~100,000+ docking runs/hour

## Use Cases

- 🔬 Drug Discovery and Virtual Screening
- 🧪 Protein-Ligand Binding Studies
- 📚 Large-Scale Chemical Library Screening
- 🎓 Educational Computational Chemistry
- 🌍 Pandemic Response (e.g., COVID-19 drug discovery)

## Contributing

We welcome contributions! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.

## 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

## Citation

If you use Docking@HOME in your research, please cite:

```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}
}
```

## Support

- 📧 Email: [email protected]
- � Issues: [HuggingFace Issues](https://huggingface.co/OpenPeerAI/DockingAtHOME/discussions)
- 🤗 Community: [HuggingFace Discussions](https://huggingface.co/OpenPeerAI/DockingAtHOME/discussions)

## Acknowledgments

- The AutoDock development team at The Scripps Research Institute
- BOINC project at UC Berkeley
- CUDPP developers
- Lonero Team for the Decentralized Internet SDK
- OpenPeer AI for Cloud Agents framework

---

**Made with ❤️ by the open-source computational chemistry community**