AMPGAN v2: Machine Learning Guided Discovery of Anti-Microbial Peptides

Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2020.11.18.388843v1?rss=1
Authors: Van Oort, C. M., Ferrell, J. B., Remington, J. M., Wshah, S., Li, J.
Abstract:
Antibiotic resistance is a critical public health problem. Each year ${sim}2.8$ million resistant infections lead to more than 35,000 deaths in the U.S. alone. Anti-microbial peptides (AMPs) show promise in treating resistant infections. But, 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. In this paper we present AMPGAN v2, a generative adversarial network (GAN) based approach for rational AMP design. Like AMPGAN, AMPGAN v2 combines data driven priors and controlled generation. These elements allow for the generation of AMP candidates tailored for specific applications. AMPGAN v2 is able to generate AMP candidates that are novel and diverse, making it an efficient AMP design tool.
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