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dc.contributor.authorWong, Felix
dc.contributor.authorZheng, Erica J.
dc.contributor.authorValeri, Jacqueline A.
dc.contributor.authorDonghia, Nina M.
dc.contributor.authorAnahtar, Melis N.
dc.contributor.authorOmori, Satotaka
dc.contributor.authorLi, Alicia
dc.contributor.authorCubillos-Ruiz1, Andres
dc.contributor.authorKrishnan, Aarti
dc.contributor.authorJin, Wengong
dc.contributor.authorManson, Abigail L.
dc.contributor.authorFriedrich, Jens
dc.contributor.authorHelbig, Ralf
dc.contributor.authorHajian, Behnoush
dc.contributor.authorFiejtek, Dawid K.
dc.contributor.authorWagner, Florence F.
dc.contributor.authorSoutter, Holly H.
dc.contributor.authorEarl, Ashlee M.
dc.contributor.authorStokes, Jonathan M.
dc.contributor.authorRenner, Lars D.
dc.contributor.authorCollins, James J.
dc.date.accessioned2023-12-20T17:02:30Z
dc.date.available2023-12-20T17:02:30Z
dc.date.issued2023-12-20
dc.identifier.issn1476-4687
dc.identifier.urihttps://hdl.handle.net/1721.1/153216
dc.description.abstractThe discovery of novel structural classes of antibiotics is urgently needed to address the ongoing antibiotic resistance crisis. Deep learning approaches have aided in exploring chemical spaces; 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.en_US
dc.language.isoen_US
dc.publisherSpringer Natureen_US
dc.relation.isversionofhttps://doi.org/10.1038/s41586-023-06887-8en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceMIT News Officeen_US
dc.titleDiscovery of a structural class of antibiotics with explainable deep learningen_US
dc.typeArticleen_US
dc.identifier.citationWong, F., Zheng, E.J., Valeri, J.A. et al. Discovery of a structural class of antibiotics with explainable deep learning. Nature (2023).en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Biological Engineeringen_US
dc.relation.journalNatureen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.date.submission2023-12-20T16:58:49Z
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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