Antibiotic resistance is a critical public health problem. Each year ~2.8 million resistant infections lead to more than 35,000 deaths in the U.S. alone. Antimicrobial peptides (AMPs) show promise in treating resistant infections. However, applications of known AMPs have encountered issues in development, production, and shelf-life. To drive the development of AMP-based treatments it is necessary to create design approaches with higher precision and selectivity towards resistant targets. Previously we developed AMPGAN and obtained proof-of-concept evidence for the generative approach to design AMPs with experimental validation. Building on the success of AMPGAN, we present AMPGAN v2 a bidirectional conditional generative adversarial network (BiCGAN) based approach for rational AMP design. AMPGAN v2 uses generator-discriminator dynamics to learn data driven priors and controls generation using conditioning variables. The bidirectional component, implemented using a learned encoder to map data samples into the latent space of the generator, aids iterative manipulation of candidate peptides. These elements allow AMPGAN v2 to generate of candidates that are novel, diverse, and tailored for specific applications---making it an efficient AMP design tool.

中文翻译：

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AMPGAN v2：机器学习指导的抗微生物肽的发现

抗生素耐药性是关键的公共卫生问题。仅在美国，每年约有280万抗药性感染导致35,000多人死亡。抗菌肽（AMPs）在治疗耐药性感染方面显示出希望。但是，已知AMP的应用在开发，生产和保质期内遇到问题。为了推动基于AMP的治疗方法的发展，必须创建对耐药性靶标具有更高精确度和选择性的设计方法。以前，我们开发了AMPGAN，并获得了通过实验验证设计AMP的生成方法的概念证据。在AMPGAN成功的基础上，我们提出了AMPGAN v2，这是一种基于双向条件生成对抗网络（BiCGAN）的合理AMP设计方法。AMPGAN v2使用生成器判别器动力学来学习数据驱动的先验知识，并使用条件变量控制生成。使用学习过的编码器将数据样本映射到生成器的潜在空间中实现的双向组件，有助于候选肽的迭代操作。这些元素使AMPGAN v2能够生成新颖，多样且针对特定应用量身定制的候选人-使其成为高效的AMP设计工具。