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https://github.com/aqlaboratory/openfold

Trainable, memory-efficient, and GPU-friendly PyTorch reproduction of AlphaFold 2
https://github.com/aqlaboratory/openfold

alphafold2 protein-structure pytorch

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Trainable, memory-efficient, and GPU-friendly PyTorch reproduction of AlphaFold 2

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README 

        
![header ](imgs/of_banner.png)

_Figure: Comparison of OpenFold and AlphaFold2 predictions to the experimental structure of PDB 7KDX, chain B._



# OpenFold



A faithful but trainable PyTorch reproduction of DeepMind's 

[AlphaFold 2](https://github.com/deepmind/alphafold).



# Documentation

See our new home for docs at [openfold.readthedocs.io](https://openfold.readthedocs.io/en/latest/), with instructions for installation and model inference/training.



Much of the content from this page may be found [here.](https://github.com/aqlaboratory/openfold/blob/main/docs/source/original_readme.md)



## Copyright Notice



While AlphaFold's and, by extension, OpenFold's source code is licensed under

the permissive Apache Licence, Version 2.0, DeepMind's pretrained parameters 

fall under the CC BY 4.0 license, a copy of which is downloaded to 

`openfold/resources/params` by the installation script. Note that the latter

replaces the original, more restrictive CC BY-NC 4.0 license as of January 2022.



## Contributing



If you encounter problems using OpenFold, feel free to create an issue! We also

welcome pull requests from the community.



## Citing this Work



Please cite our paper:



```bibtex

@article {Ahdritz2022.11.20.517210,

	author = {Ahdritz, Gustaf and Bouatta, Nazim and Floristean, Christina and Kadyan, Sachin and Xia, Qinghui and Gerecke, William and O{\textquoteright}Donnell, Timothy J and Berenberg, Daniel and Fisk, Ian and Zanichelli, Niccolò and Zhang, Bo and Nowaczynski, Arkadiusz and Wang, Bei and Stepniewska-Dziubinska, Marta M and Zhang, Shang and Ojewole, Adegoke and Guney, Murat Efe and Biderman, Stella and Watkins, Andrew M and Ra, Stephen and Lorenzo, Pablo Ribalta and Nivon, Lucas and Weitzner, Brian and Ban, Yih-En Andrew and Sorger, Peter K and Mostaque, Emad and Zhang, Zhao and Bonneau, Richard and AlQuraishi, Mohammed},

	title = {{O}pen{F}old: {R}etraining {A}lpha{F}old2 yields new insights into its learning mechanisms and capacity for generalization},

	elocation-id = {2022.11.20.517210},

	year = {2022},

	doi = {10.1101/2022.11.20.517210},

	publisher = {Cold Spring Harbor Laboratory},

	URL = {https://www.biorxiv.org/content/10.1101/2022.11.20.517210},

	eprint = {https://www.biorxiv.org/content/early/2022/11/22/2022.11.20.517210.full.pdf},

	journal = {bioRxiv}

}

```

If you use OpenProteinSet, please also cite:



```bibtex

@misc{ahdritz2023openproteinset,

      title={{O}pen{P}rotein{S}et: {T}raining data for structural biology at scale}, 

      author={Gustaf Ahdritz and Nazim Bouatta and Sachin Kadyan and Lukas Jarosch and Daniel Berenberg and Ian Fisk and Andrew M. Watkins and Stephen Ra and Richard Bonneau and Mohammed AlQuraishi},

      year={2023},

      eprint={2308.05326},

      archivePrefix={arXiv},

      primaryClass={q-bio.BM}

}

```

Any work that cites OpenFold should also cite [AlphaFold](https://www.nature.com/articles/s41586-021-03819-2) and [AlphaFold-Multimer](https://www.biorxiv.org/content/10.1101/2021.10.04.463034v1) if applicable.