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 maximum likelihood 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 35% 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.

研究通过学习平滑能量函数、使用 Langevin Markov chain Monte Carlo 对平滑数据流形进行采样并通过一步去噪投影回真实数据流形的方法来解决离散生成模型的训练和采样困难，并引入分布符合度分数对蛋白质生成模型进行了评估。