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chroma-pytorch

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Implementation of Chroma, generative models of protein using DDPM and GNNs, in Pytorch

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- - python-publish.yml
- __init__.py
- chroma_pytorch.py
- semantic_conditioner.py
.gitignore
chroma.png
LICENSE
README.md
rfdiffusion.gif
setup.py

# Installation Guide

1. Get the code

git clone https://github.com/lucidrains/chroma-pytorch

Downloads the entire project code from GitHub to your computer.

cd chroma-pytorch

Moves into the project folder you just downloaded.

2. Python

Easy Recommended

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

pip install .

Installs the package published on PyPI directly — no need to clone the source.

python <실행할 파일명>.py # README에서 정확한 실행 파일명을 확인하세요

Runs the Python script (or module).

✅ If it runs without errors and prints output in the terminal, it worked.

// repository documentation

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<img src="./chroma.png" width="550px"></img>

<i>figure 1 in <a href="https://www.biorxiv.org/content/10.1101/2022.12.01.518682v1">paper</a></i>

<img src="./rfdiffusion.gif" width="450px"></img>

*generating a protein that binds to spike protein of coronavirus - Baker lab's concurrent RFDiffusion work*

## Chroma - Pytorch (wip)

Implementation of <a href="https://generatebiomedicines.com/chroma">Chroma</a>, generative model of proteins using DDPM and GNNs, in Pytorch. <a href="https://www.bakerlab.org/2022/11/30/diffusion-model-for-protein-design/">Concurrent work</a> seems to suggest we have a slight lift-off applying denoising diffusion probabilistic models to protein design. Will also incorporate <a href="https://arxiv.org/abs/2208.04202">self-conditioning</a>, applied successfully by Baker lab in RFDiffusion.

<a href="https://stephanheijl.com/rfdiffusion.html">Explanation by Stephan Heijl</a>

If you are interested in open sourcing works like these out in the wild, please consider joining <a href="https://openbioml.org/">OpenBioML</a>

## Todo

- [ ] use <a href="https://huggingface.co/mrm8488/galactica-125m">galactica</a>

## Citations

```bibtex
@misc{
    title   = {Illuminating protein space with a programmable generative model},
    author  = {John Ingraham, Max Baranov, Zak Costello, Vincent Frappier, Ahmed Ismail, Shan Tie, Wujie Wang, Vincent Xue, Fritz Obermeyer, Andrew Beam, Gevorg Grigoryan},    
    year    = {2022},
    url     = {https://cdn.generatebiomedicines.com/assets/ingraham2022.pdf}
}
```