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  3. A generative deep learning approach to de novo antibiotic design

A generative deep learning approach to de novo antibiotic design

  • Cell. 2025 Aug 7:S0092-8674(25)00855-4. doi: 10.1016/j.cell.2025.07.033.
Aarti Krishnan 1,  Melis N Anahtar 2,  Jacqueline A Valeri 3,  Wengong Jin 4,  Nina M Donghia 5,  Leif Sieben 6,  Andreas Luttens 7,  Yu Zhang 3,  Seyed Majed Modaresi 3,  Andrew Hennes 8,  Jenna Fromer 9,  Parijat Bandyopadhyay 7,  Jonathan C Chen 7,  Danyal Rehman 10,  Ronak Desai 11,  Paige Edwards 7,  Ryan S Lach 12,  Marie-Stéphanie Aschtgen 13,  Margaux Gaborieau 13,  Massimiliano Gaetani 14,  Samantha G Palace 15,  Satotaka Omori 12,  Lutete Khonde 16,  Yurii S Moroz 17,  Bruce Blough 18,  Chunyang Jin 18,  Edmund Loh 19,  Yonatan H Grad 15,  Amir Ata Saei 13,  Connor W Coley 20,  Felix Wong 21,  James J Collins 22
Affiliations

Affiliations

  • 1 Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Institute for Medical Engineering & Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA.
  • 2 Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Institute for Medical Engineering & Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA; Departments of Pathology and Medicine, Massachusetts General Hospital, Boston, MA 02114, USA.
  • 3 Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Institute for Medical Engineering & Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA.
  • 4 Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, USA.
  • 5 Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA.
  • 6 Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Institute for Medical Engineering & Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA; Department of Chemistry and Applied Biosciences, ETH Zürich, 8093 Zürich, Switzerland.
  • 7 Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Institute for Medical Engineering & Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
  • 8 Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA.
  • 9 Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
  • 10 Mila-Quebec AI Institute, Montréal, QC H2S 3H1, Canada.
  • 11 Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Harvard Medical School, Boston, MA 02115, USA.
  • 12 Integrated Biosciences, Inc., Redwood City, CA 94065, USA.
  • 13 Department of Microbiology, Tumor and Cell Biology, Karolinska Institute, 171 77 Stockholm, Sweden.
  • 14 Division of Chemistry I, Department of Medical Biochemistry and Biophysics, Karolinska Institute, 17 177 Stockholm, Sweden; Chemical Proteomics Unit, Science for Life Laboratory (SciLifeLab), 171 77Stockholm, Sweden.
  • 15 Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.
  • 16 Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
  • 17 Enamine Ltd., 67 Winston Churchill Street, Kyiv 02094, Ukraine; Chemspace LLC, 85 Winston Churchill Street, Kyiv 02094, Ukraine; Department of Chemistry, Taras Shevchenko National University of Kyiv, 60 Volodymyrska Street, Kyiv 01601, Ukraine.
  • 18 Center for Drug Discovery, RTI International, Research Triangle Park, Durham, NC 27713, USA.
  • 19 Department of Microbiology, Tumor and Cell Biology, Karolinska Institute, 171 77 Stockholm, Sweden; Clinical Microbiology, Karolinska University Hospital, 171 76 Stockholm, Sweden.
  • 20 Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
  • 21 Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Institute for Medical Engineering & Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Integrated Biosciences, Inc., Redwood City, CA 94065, USA.
  • 22 Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Institute for Medical Engineering & Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA. Electronic address: jimjc@mit.edu.
PMID: 40816267 DOI: 10.1016/j.cell.2025.07.033
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.

Keywords

Neisseria gonorrhoeae; Staphylococcus aureus; antibiotics; bacterial infection; de novo design; drug discovery; fragments; generative artificial intelligence; graph neural networks; machine learning.

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