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# Docking@HOME
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**Distributed and Parallel Molecular Docking Platform**
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[](https://www.gnu.org/licenses/gpl-3.0)
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[](https://www.gnu.org/licenses/gpl-3.0)
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[](https://huggingface.co/)
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## Overview
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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.
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### Key Features
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- 𧬠**AutoDock Integration**: Uses AutoDock Suite 4.2.6 for molecular docking simulations
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- π **GPU Acceleration**: CUDPP-powered parallel processing for enhanced performance
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- π **Distributed Computing**: BOINC framework for volunteer computing at scale
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- π **Decentralized Networking**: Distributed Network Settings-based coordination using the Decentralized Internet SDK
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- π€ **AI Orchestration**: Cloud Agents for intelligent task distribution and optimization
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- π **HuggingFace Integration**: Model cards and datasets for reproducible research
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## Architecture
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```
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βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
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β Docking@HOME Platform β
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βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ€
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β ββββββββββββββββ ββββββββββββββββ βββββββββββββββββββ β
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β β Cloud Agents β β Decentralizedβ β BOINC Server β β
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β β (AI Routing) ββββ€ Internet ββββ€ (Task Mgmt) β β
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β ββββββββββββββββ ββββββββββββββββ βββββββββββββββββββ β
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β βΌ βΌ β
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β ββββββββββββββββββββββββββββββββββββββββββββββββββββββββ β
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β β Distributed Worker Nodes (BOINC Clients) β β
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β β ββββββββββββββββ ββββββββββββββββ β β
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β β β AutoDock βββββββββΊβ CUDPP β β β
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β β β (Docking) β β (GPU Accel) β β β
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β β ββββββββββββββββ ββββββββββββββββ β β
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β ββββββββββββββββββββββββββββββββββββββββββββββββββββββββ β
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βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
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```
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## Components
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### 1. AutoDock Suite (v4.2.6)
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Core molecular docking engine that predicts binding modes and affinities of small molecules to protein targets.
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### 2. CUDPP (CUDA Data Parallel Primitives Library)
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Provides GPU-accelerated parallel primitives for enhancing AutoDock's computational performance.
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### 3. BOINC (Berkeley Open Infrastructure for Network Computing)
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Distributed computing middleware that manages volunteer computing resources globally.
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### 4. The Decentralized Internet SDK
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Enables Distributed Network Settings-based coordination, ensuring transparency and decentralization of task distribution.
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### 5. Cloud Agents
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AI-powered orchestration layer that optimizes task scheduling and resource allocation based on workload characteristics.
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## Authors & Contributors
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- **OpenPeer AI** - AI/ML Integration & Cloud Agents
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- **Riemann Computing Inc.** - Distributed Computing Architecture
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- **Bleunomics** - Bioinformatics & Drug Discovery Expertise
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- **Andrew Magdy Kamal** - Project Lead & System Integration
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## Installation
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### Prerequisites
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- C++ compiler (GCC 7+ or MSVC 2019+)
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- CUDA Toolkit 11.0+ (for GPU acceleration)
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- Python 3.8+
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- Node.js 16+ (for the Decentralized Internet SDK)
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- BOINC client/server software
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### Build Instructions
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```bash
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# Clone the repository
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git clone https://huggingface.co/OpenPeerAI/DockingAtHOME
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cd DockingAtHOME
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# Initialize submodules
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git submodule update --init --recursive
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# Build the project
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mkdir build && cd build
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cmake ..
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make -j$(nproc)
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# Install
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sudo make install
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```
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### Docker Installation
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```bash
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docker pull your-org/docking-at-home:latest
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docker run -d --gpus all your-org/docking-at-home:latest
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```
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## Quick Start
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### Web GUI (Recommended!)
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```bash
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# Install dependencies
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pip install -r requirements.txt
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# Start the GUI server
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python start.py
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# Open browser to: http://localhost:8080
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```
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The GUI provides:
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- π±οΈ **Drag-and-drop** file upload
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- π **Real-time** progress monitoring
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- π **Live statistics** dashboard
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- π― **Interactive** job management
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- π± **Responsive** design
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### Command Line
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```bash
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# Run docking from terminal
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docking-at-home dock -l molecule.pdbqt -r protein.pdbqt
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# Start server
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docking-at-home server --port 8080
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# Start worker
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docking-at-home worker --local
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```
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### Python API
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```python
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from docking_at_home.server import job_manager, initialize_server
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import asyncio
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async def main():
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await initialize_server()
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job_id = await job_manager.submit_job(
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ligand_file="molecule.pdbqt",
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receptor_file="protein.pdbqt",
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num_runs=100,
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use_gpu=True
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)
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# Monitor progress
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while True:
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job = job_manager.get_job(job_id)
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if job["status"] == "completed":
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print(f"Best energy: {job['results']['best_energy']}")
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break
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await asyncio.sleep(1)
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asyncio.run(main())
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```
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### Running on Localhost
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```bash
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# Start the local server
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docking-at-home server --port 8080
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# In another terminal, run the worker
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docking-at-home worker --local
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```
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## Configuration
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Configuration files are located in `config/`:
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- `autodock.conf` - AutoDock parameters
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- `boinc_server.conf` - BOINC server settings
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- `gpu_config.conf` - CUDPP and GPU settings
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- `decentralized.conf` - Distributed Network Settings
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- `cloud_agents.conf` - AI orchestration parameters
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## API Documentation
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Full API documentation is available at [docs/API.md](docs/API.md)
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## Performance
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On a typical configuration:
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- **CPU-only**: ~100 docking runs/hour
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- **Single GPU (RTX 3090)**: ~2,000 docking runs/hour
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- **Distributed (1000 nodes)**: ~100,000+ docking runs/hour
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## Use Cases
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- π¬ Drug Discovery and Virtual Screening
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- π§ͺ Protein-Ligand Binding Studies
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- π Large-Scale Chemical Library Screening
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- π Educational Computational Chemistry
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- π Pandemic Response (e.g., COVID-19 drug discovery)
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## Contributing
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We welcome contributions! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.
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## License
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This project is licensed under the GNU General Public License v3.0 - see [LICENSE](LICENSE) for details.
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Individual components retain their original licenses:
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- AutoDock: GNU GPL v2
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- BOINC: GNU LGPL v3
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- CUDPP: BSD License
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## Citation
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If you use Docking@HOME in your research, please cite:
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```bibtex
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@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},
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year={2025},
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url={https://huggingface.co/OpenPeerAI/DockingAtHOME}
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}
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```
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## HuggingFace Integration
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Model cards and datasets are available at:
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- π€ [https://huggingface.co/OpenPeerAI/DockingAtHOME](https://huggingface.co/OpenPeerAI/DockingAtHOME)
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## Support
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- π§ Email: andrew@bleunomics.com
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- οΏ½ Issues: [HuggingFace Issues](https://huggingface.co/OpenPeerAI/DockingAtHOME/discussions)
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- π€ Community: [HuggingFace Discussions](https://huggingface.co/OpenPeerAI/DockingAtHOME/discussions)
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## Acknowledgments
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- The AutoDock development team at The Scripps Research Institute
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- BOINC project at UC Berkeley
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- CUDPP developers
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- Lonero Team for the Decentralized Internet SDK
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- OpenPeer AI for Cloud Agents framework
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---
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**Made with β€οΈ by the open-source computational chemistry community**
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