https://github.com/lucidrains/chroma-pytorch
Implementation of Chroma, generative models of protein using DDPM and GNNs, in Pytorch
https://github.com/lucidrains/chroma-pytorch
artificial-intelligence deep-learning denoising-diffusion graph-neural-networks protein-design
Last synced: 11 months ago
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Implementation of Chroma, generative models of protein using DDPM and GNNs, in Pytorch
- Host: GitHub
- URL: https://github.com/lucidrains/chroma-pytorch
- Owner: lucidrains
- License: mit
- Created: 2022-12-02T01:57:50.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-12-27T19:25:56.000Z (over 3 years ago)
- Last Synced: 2024-12-10T16:50:26.947Z (over 1 year ago)
- Topics: artificial-intelligence, deep-learning, denoising-diffusion, graph-neural-networks, protein-design
- Language: Python
- Homepage:
- Size: 4.97 MB
- Stars: 158
- Watchers: 19
- Forks: 23
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
figure 1 in paper
*generating a protein that binds to spike protein of coronavirus - Baker lab's concurrent RFDiffusion work*
## Chroma - Pytorch (wip)
Implementation of Chroma, generative model of proteins using DDPM and GNNs, in Pytorch. Concurrent work seems to suggest we have a slight lift-off applying denoising diffusion probabilistic models to protein design. Will also incorporate self-conditioning, applied successfully by Baker lab in RFDiffusion.
If you are interested in open sourcing works like these out in the wild, please consider joining OpenBioML
## Todo
- [ ] use galactica
## 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}
}
```