期刊
CELL
卷 180, 期 4, 页码 688-+出版社
CELL PRESS
DOI: 10.1016/j.cell.2020.01.021
关键词
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资金
- Abdul Latif Jameel Clinic for Machine Learning in Health
- Defence Threat Reduction Agency [HDTRA1-151-0051, HR00111920025]
- Broad Institute of MIT and Harvard
- DARPA Make-It program under contract Army Research Office [W911NF-16-2-0023]
- Canadian Institutes of Health Research [FRN 143215]
- Canadian Foundation for Innovation
- Canada Research Chairs Program (Tier 1)
- Banting Fellowships Program [393360]
- Canadian Institutes of Health Research
- Human Frontier Science Program [LT000975/2016-L]
- Broad Institute Tuberculosis Donor Group
- Pershing Square Foundation
- Swiss National Science Foundation [P2ELP2_181884]
- NIH Early Investigator Award [DP5-OD-024590]
- National Science Foundation Graduate Research Fellowship Program [1122374]
- Swiss National Science Foundation (SNF) [P2ELP2_181884] Funding Source: Swiss National Science Foundation (SNF)
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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