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A Generative Deep Learning Approach to de Novo Antibiotic Design by Aarti Krishnan & Melis N. Anahtar & 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... is a book available to read on EtoBox.

What is A Generative Deep Learning Approach to de Novo Antibiotic Design about?

Cell, Corrected proof. doi:10.1016/j.cell.2025.07.033SUMMARYThe 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 throughtwo approaches: a fragment-based method to comprehensively screen >107 chemical fragments in silicoagainst 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 ofaction 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 asantibacterial. Our approach enables the generative deep-learning-guided design of de novo antibi

Author

Aarti Krishnan & Melis N. Anahtar & 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...

Publisher

x

Published

2025

Language

EN