@inproceedings{
frey2023protein,
title={Protein Discovery with Discrete Walk-Jump Sampling},
author={Nathan Frey and Dan Berenberg and Karina Zadorozhny and Joseph Kleinhenz and Julien Lafrance-Vanasse and Isidro Hotzel and Yan Wu and Stephen Ra and Richard Bonneau and Kyunghyun Cho and Vladimir Gligorijevic and Saeed Saremi},
booktitle={NeurIPS 2023 Generative AI and Biology (GenBio) Workshop},
year={2023},
url={https://openreview.net/forum?id=y14a0nIECr}
}
We resolve difficulties in training and sampling from a discrete generative model by learning a smoothed energy function, sampling from the smoothed data manifold with Langevin Markov chain Monte Carlo (MCMC), and projecting back to the true data manifold with one-step denoising. Our $\textit{Discrete Walk-Jump Sampling}$ formalism combines the contrastive divergence training of an energy-based model and improved sample quality of a score-based model, while simplifying training and sampling by requiring only a single noise level. We evaluate the robustness of our approach on generative modeling of antibody proteins and introduce the $\textit{distributional conformity score}$ to benchmark protein generative models. By optimizing and sampling from our models for the proposed distributional conformity score, 97-100\% of generated samples are successfully expressed and purified and 70\% of functional designs show equal or improved binding affinity compared to known functional antibodies on the first attempt in a single round of laboratory experiments. We also report the first demonstration of long-run fast-mixing MCMC chains where diverse antibody protein classes are visited in a single MCMC chain.
Protein Discovery with Discrete Walk-Jump Sampling
Nathan Frey Dan Berenberg Karina Zadorozhny Joseph Kleinhenz Julien Lafrance-Vanasse Isidro Hotzel Yan Wu Stephen Ra Richard Bonneau Kyunghyun Cho Vladimir Gligorijevic Saeed Saremi