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<!DOCTYPE html>
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<title>Walk-Jump Sampling </title>
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<h1 class="title is-1 publication-title">Walk-Jump Sampling for Protein Discovery</h1>
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<span class="author-block">
<a href="" target="_blank">Nathan C. Frey</a><sup>*</sup>, Dan Berenberg</a><sup>*</sup>, ... Saeed Saremi,</span>
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<span class="author-block">Prescient Design<br>Genentech</span>
<span class="eql-cntrb"><small><br><sup>*</sup>Indicates Equal Contribution</small></span>
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<h2 class="title is-3">Abstract</h2>
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<p>
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 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 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.
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Discrete Walk-Jump Sampling wins Outstanding Paper award at ICLR.
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Selected samples from a single Markov chain Monte Carlo sampling run of discrete Walk-Jump sampling
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<h2 class="title">BibTeX</h2>
<pre><code>@article{frey2023protein,
title={Protein discovery with discrete walk-jump sampling},
author={Frey, Nathan C and Berenberg, Daniel and Zadorozhny, Karina and Kleinhenz, Joseph and Lafrance-Vanasse, Julien and Hotzel, Isidro and Wu, Yan and Ra, Stephen and Bonneau, Richard and Cho, Kyunghyun and others},
journal={arXiv preprint arXiv:2306.12360},
year={2023}
}</code></pre>
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</section>
<!--End BibTex citation -->
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