protenix/openfold_local/README.md

## openfold_local

This is copy from [openfold](https://github.com/aqlaboratory/openfold), commit id: [bb3f51](https://github.com/aqlaboratory/openfold/commit/bb3f51e5a2cf2d5e3b709fe8f7d7a083c870222e)

Openfold is a great work. We try to reuse it when building models. However, A few modifications has been made for our protenix project.

 * In [protenix/openfold_local/model/primitives.py](model/primitives.py), we add a custom [`Layernorm`](../model/layer_norm/) implementation, it accelerate protenix about 30%-50% during different training stages

If you use our work, please also cite Openfold:

```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}
}
```