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
- December 2024: Successful migration to PyTorch, with validation confirming alignment with the original implementation
Follow the setup instructions provided in the AlphaFold3 README to ensure dependencies are correctly installed and the AlphaFold 3 model parameters are downloaded.
Install xfold using pip:
pip install xfoldExecute 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=./outputThe xfold source code is licensed under the Apache License, Version 2.0.
This project may include code adapted from or inspired by AlphaFold 3 source code, which is licensed under Apache-2.0.
AlphaFold 3 model parameters/weights are not included in this repository and are subject to Google DeepMind's separate AlphaFold 3 Model Parameters Terms of Use. 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.
We welcome contributions from the research community! Open an issue or send a pull request.