Discovery of a structural class of antibiotics with explainable deep learning

Felix Wong, Erica J. Zheng, Jacqueline A. Valeri, Nina M. Donghia, Melis N. Anahtar, Satotaka Omori, Alicia Li, Andres Cubillos-Ruiz, Aarti Krishnan, Wengong Jin, Abigail L. Manson, Jens Friedrichs, Ralf Helbig, Behnoush Hajian, Dawid K. Fiejtek, Florence F. Wagner, Holly H. Soutter, Ashlee M. Earl, Jonathan M. Stokes, Lars D. Renner and James J. Collins ()
Additional contact information
Felix Wong: Broad Institute of MIT and Harvard
Erica J. Zheng: Broad Institute of MIT and Harvard
Jacqueline A. Valeri: Broad Institute of MIT and Harvard
Nina M. Donghia: Harvard University
Melis N. Anahtar: Broad Institute of MIT and Harvard
Satotaka Omori: Broad Institute of MIT and Harvard
Alicia Li: Integrated Biosciences
Andres Cubillos-Ruiz: Broad Institute of MIT and Harvard
Aarti Krishnan: Broad Institute of MIT and Harvard
Wengong Jin: Broad Institute of MIT and Harvard
Abigail L. Manson: Broad Institute of MIT and Harvard
Jens Friedrichs: Leibniz Institute of Polymer Research and the Max Bergmann Center of Biomaterials
Ralf Helbig: Leibniz Institute of Polymer Research and the Max Bergmann Center of Biomaterials
Behnoush Hajian: Center for the Development of Therapeutics, Broad Institute of MIT and Harvard
Dawid K. Fiejtek: Center for the Development of Therapeutics, Broad Institute of MIT and Harvard
Florence F. Wagner: Center for the Development of Therapeutics, Broad Institute of MIT and Harvard
Holly H. Soutter: Center for the Development of Therapeutics, Broad Institute of MIT and Harvard
Ashlee M. Earl: Broad Institute of MIT and Harvard
Jonathan M. Stokes: Broad Institute of MIT and Harvard
Lars D. Renner: Leibniz Institute of Polymer Research and the Max Bergmann Center of Biomaterials
James J. Collins: Broad Institute of MIT and Harvard

Nature, 2024, vol. 626, issue 7997, 177-185

Abstract: Abstract The discovery of novel structural classes of antibiotics is urgently needed to address the ongoing antibiotic resistance crisis1–9. Deep learning approaches have aided in exploring chemical spaces1,10–15; these typically use black box models and do not provide chemical insights. Here we reasoned that the chemical substructures associated with antibiotic activity learned by neural network models can be identified and used to predict structural classes of antibiotics. We tested this hypothesis by developing an explainable, substructure-based approach for the efficient, deep learning-guided exploration of chemical spaces. We determined the antibiotic activities and human cell cytotoxicity profiles of 39,312 compounds and applied ensembles of graph neural networks to predict antibiotic activity and cytotoxicity for 12,076,365 compounds. Using explainable graph algorithms, we identified substructure-based rationales for compounds with high predicted antibiotic activity and low predicted cytotoxicity. We empirically tested 283 compounds and found that compounds exhibiting antibiotic activity against Staphylococcus aureus were enriched in putative structural classes arising from rationales. Of these structural classes of compounds, one is selective against methicillin-resistant S. aureus (MRSA) and vancomycin-resistant enterococci, evades substantial resistance, and reduces bacterial titres in mouse models of MRSA skin and systemic thigh infection. Our approach enables the deep learning-guided discovery of structural classes of antibiotics and demonstrates that machine learning models in drug discovery can be explainable, providing insights into the chemical substructures that underlie selective antibiotic activity.

Date: 2024
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DOI: 10.1038/s41586-023-06887-8

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