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Cell DOI:10.1016/j.cell.2020.01.021

A Deep Learning Approach to Antibiotic Discovery.

Publication TypeJournal Article
Year of Publication2020
AuthorsStokes, JM, Yang, K, Swanson, K, Jin, W, Cubillos-Ruiz, A, Donghia, NM, MacNair, CR, French, S, Carfrae, LA, Bloom-Ackerman, Z, Tran, VM, Chiappino-Pepe, A, Badran, AH, Andrews, IW, Chory, EJ, Church, GM, Brown, ED, Jaakkola, TS, Barzilay, R, Collins, JJ
JournalCell
Volume180
Issue4
Pages688-702.e13
Date Published2020 Feb 20
ISSN1097-4172
Abstract

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.

DOI10.1016/j.cell.2020.01.021
Pubmed

http://www.ncbi.nlm.nih.gov/pubmed/32084340?dopt=Abstract

Alternate JournalCell
PubMed ID32084340