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https://github.com/shenggan/xfold
Democratizing AlphaFold3
https://github.com/shenggan/xfold
alphafold3 gpu-acceleration pytorch
Last synced: about 1 month ago
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Democratizing AlphaFold3
- Host: GitHub
- URL: https://github.com/shenggan/xfold
- Owner: Shenggan
- License: other
- Created: 2024-11-20T11:31:04.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2024-12-03T07:19:30.000Z (about 1 month ago)
- Last Synced: 2024-12-03T08:24:16.240Z (about 1 month ago)
- Topics: alphafold3, gpu-acceleration, pytorch
- Language: Python
- Homepage:
- Size: 2.14 MB
- Stars: 4
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# xfold: Democratize AlphaFold3
xfold is an open-source, PyTorch-based reimplementation of AlphaFold3, designed to accelerate protein structure prediction research and make cutting-edge AI technology more accessible to the scientific community.
Future developments for xfold will focus on integrating cutting-edge performance optimization techniques and advanced parallelization strategies. Our ultimate goal is to democratize AlphaFold3, empowering a broader researcher to contribute to and benefit from this transformative technology.
Visualization result comparison of 2pv7
## Recent Developments 🚀
* **December 2024**: Successful migration to PyTorch, with validation confirming alignment with the original implementation
## Getting Started
### Step 1: Prepare the Environment
Follow the setup instructions provided in the [AlphaFold3 README](https://github.com/google-deepmind/alphafold3) to ensure dependencies are correctly installed and the AlphaFold 3 model parameters are downloaded.
### Step 2: Install xfold
Install xfold using pip:
```bash
pip install xfold
```
### Step 3: Running Predictions
Execute protein structure predictions with the following command:
```bash
python run_alphafold.py \
--db_dir=$PATH_TO_AF3_DATASET \
--json_path=./fold_input.json \
--model_dir=$$PATH_TO_AF3_MODEL \
--output_dir=./output
```
## Acknowledgments
We extend our gratitude to AlphaFold3 for open-sourcing their inference code and model weights, which has significantly advanced scientific research. xfold is provided exclusively for educational and research purposes. Users are kindly requested to review and comply with the AlphaFold3 license, available at https://github.com/google-deepmind/alphafold3?tab=readme-ov-file#licence-and-disclaimer.
## Contributing
We welcome contributions from the research community! Open an [issue](https://github.com/shenggan/xfold/issues) or send a [pull request](https://github.com/shenggan/xfold/pulls).