A Deep Learning Approach to Antibiotic Discovery
Open accessAbstract
Due to the rapid emergence of antibiotic-resistant bacteria, there is a growing need to discover new antibiotics. To address this challenge, we trained a deep neural network capable of predicting molecules with antibacterial activity. We performed predictions on multiple chemical libraries and discovered a molecule from the Drug Repurposing Hub-halicin-that is structurally divergent from conventional antibiotics and displays bactericidal activity against a wide phylogenetic spectrum of pathogens including Mycobacterium tuberculosis and carbapenem-resistant Enterobacteriaceae. Halicin also effectively treated Clostridioides difficile and pan-resistant Acinetobacter baumannii infections in murine models. Additionally, from a discrete set of 23 empirically tested predictions from >107 million molecules curated from the ZINC15 database, our model identified eight antibacterial compounds that are structurally distant from known antibiotics. This work highlights the utility of deep learning approaches to expand our antibiotic arsenal through the discovery of structurally distinct antibacterial molecules.
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- Authors
- Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M., MacNair, C. R., French, S., Carfrae, L. A., Bloom-Ackermann, Z., Tran, V. M., Chiappino-Pepe, A., Badran, A. H., Andrews, I. W., Chory, E. J., Church, G. M., Brown, E. D., Jaakkola, T. S., Barzilay, R., & Collins, J. J.
- Affiliations
- Broad Institute (US)
- MIT Computer Science and Artificial Intelligence Laboratory (US)
- Harvard University (US)
- Michael G. DeGroote Institute for Infectious Disease Research (CA)
- Corresponding author
- Barzilay, Regina
- Collins, James J.
- ORCID
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2020-02
- Volume
- 180
- Issue
- 4
- Pages
- 688-702.e13
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC8349178
- OpenAlex
- W3007309629
- Cited by
- 2,463
- References
- 69
- Citations by year
- 2026: 509 · 2025: 439 · 2024: 422 · 2023: 290 · 2022: 295 · 2021: 329 · 2020: 171 · 2019: 2
- FWCI
- 135.13
- Type
- journal article
- Language
- en
- Update notices
- erratum (2020-04-16)
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-nd)
- Funders
- Canadian Institutes of Health Research
- National Institutes of Health
- Canada Foundation for Innovation
- Canada Research Chairs
- National Science Foundation
- Defense Threat Reduction Agency
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
- Broad Institute
- Defense Advanced Research Projects Agency
- Pershing Square Foundation
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Health Sciences › Medicine › Pharmacology › Microbial Natural Products and Biosynthesis
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Keywords
- antibiotic discovery, antibacterial activity prediction, deep neural network, drug repurposing, antibiotic resistance, Mycobacterium tuberculosis, carbapenem-resistant Enterobacteriaceae, Clostridium difficile, Acinetobacter baumannii, ZINC15, structure-activity prediction
- MeSH terms
- Machine Learning, Cheminformatics, Animals, Anti-Bacterial Agents, Mice, Inbred BALB C, Mice, Inbred C57BL, Mycobacterium tuberculosis, Thiadiazoles, Clostridioides difficile, Acinetobacter baumannii, Mice, Small Molecule Libraries, Drug Discovery, Databases, Chemical
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