Given the emerging global threat of antimicrobial resistance, new methods for
next-generation antimicrobial design are urgently needed. We report a peptide
generation framework PepCVAE, based on a semi-supervised variational
autoencoder (VAE) model, for designing novel antimicrobial peptide (AMP)
sequences. Our model learns a rich latent space of the biological peptide
context by taking advantage of abundant, unlabeled peptide sequences. The model
further learns a disentangled antimicrobial attribute space by using the
feedback from a jointly trained AMP classifier that uses limited labeled
instances. The disentangled representation allows for controllable generation
of AMPs. Extensive analysis of the PepCVAE-generated sequences reveals superior
performance of our model in comparison to a plain VAE, as PepCVAE generates
novel AMP sequences with higher long-range diversity, while being closer to the
training distribution of biological peptides. These features are highly desired
in next-generation antimicrobial design.

本论文提出了基于半监督变分自编码器模型的 Peptide Generation Framework（PepCVAE），用于设计新型抗菌肽（AMP）序列，通过大量未标记的肽序列学习一种丰富的生物肽上下文的潜在空间，同时通过 AMP 分类器的反馈学习出一种分离的抗菌属性空间，生成的 AMP 序列表现优于标准 VAE，具有高度多样性，是下一代抗菌设计中非常理想的选择。