A generative deep learning approach to de novo antibiotic design
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A-generative-deep-learning-approach-to-de-novo-antibiotic-design_2025.pdf
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Author(s)
Krishnan, Aarti
Anahtar, Melis N.
Valeri, Jacqueline A.
Jin, Wengong
Donghia, Nina M.
Sieben, Leif
Luttens, Andreas
Zhang, Yu
Modaresi, Seyed Majed
Hennes, Andrew
Date Issued
October 16, 2025
Journal
Cell
Publisher
Elsevier BV
Citation
Krishnan, Aarti, Anahtar, Melis N., Valeri, Jacqueline A., Jin, Wengong, Donghia, Nina M. et al. 2025. "A generative deep learning approach to de novo antibiotic design." Cell, 188 (21).
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Author's final manuscript
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.
MIT Department
Broad Institute of MIT and Harvard
Institute for Medical Engineering and Science
Massachusetts Institute of Technology. Department of Biological Engineering
Whitehead Institute for Biomedical Research
Massachusetts Institute of Technology. Department of Chemical Engineering
Massachusetts Institute of Technology. Department of Chemistry
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-ShareAlike
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DOI of Published Version
https://doi.org/10.1016/j.cell.2025.07.033
