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A Deep Learning Approach to Antibiotic Discovery.

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.

journal_name

Cell

journal_title

Cell

authors

Stokes JM,Yang K,Swanson K,Jin W,Cubillos-Ruiz A,Donghia NM,MacNair CR,French S,Carfrae LA,Bloom-Ackermann Z,Tran VM,Chiappino-Pepe A,Badran AH,Andrews IW,Chory EJ,Church GM,Brown ED,Jaakkola TS,Barzilay R,Collins J

doi

10.1016/j.cell.2020.01.021

subject

Has Abstract

pub_date

2020-02-20 00:00:00

pages

688-702.e13

issue

4

eissn

0092-8674

issn

1097-4172

pii

S0092-8674(20)30102-1

journal_volume

180

pub_type

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