A generative deep learning approach to de novo antibiotic design

ABSTRACT The antimicrobial resistance crisis necessitates structurally distinct antibiotics. While deep learning approaches can identify antibacterial compounds from existing libraries, structural novelty remains limited. Here, we developed a generative artificial intelligence framework for designing de novo antibiotics through two approaches: a fragment-based method to comprehensively screen >107 chemical fragments in silico against Neisseria gonorrhoeae or Staphylococcus aureus, subsequently expanding promising fragments, and an unconstrained de novo compound generation, each using genetic algorithms and variational autoencoders. Of 24 synthesized compounds, seven demonstrated selective antibacterial activity. Two lead compounds exhibited bactericidal efficacy against multidrug-resistant isolates with distinct mechanisms of action and reduced bacterial burden in vivo in mouse models of N. gonorrhoeae vaginal infection and methicillin-resistant S. aureus skin infection. We further validated structural analogs for both compound classes as antibacterial. Our approach enables the generative deep-learning-guided design of de novo antibiotics, providing a platform for mapping uncharted regions of chemical space.

Co-authors: Aarti Krishnan, Jacqueline A Valeri, Wengong Jin, Nina M Donghia, Leif Sieben, Andreas Luttens, Yu Zhang, Seyed Majed Modaresi, Andrew Hennes, Jenna Fromer, Parijat Bandyopadhyay, Jonathan C Chen, Danyal Rehman, Ronak Desai, Paige Edwards, Ryan S Lach, Marie-Stéphanie Aschtgen, Margaux Gaborieau, Massimiliano Gaetani, Samantha G Palace, Satotaka Omori, Lutete Khonde, Yurii S Moroz, Bruce Blough, Chunyang Jin, Edmund Loh, Yonatan H Grad, Amir Ata Saei, Connor W Coley, Felix Wong, James J Collins