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 to ensure dependencies are correctly installed and the AlphaFold 3 model parameters are downloaded.

Step 2: Install xfold

Install xfold using pip:

pip install xfold

Step 3: Running Predictions

Execute protein structure predictions with the following command:

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 or send a pull request.