A Deep Learning Approach to Antibiotic Discovery.

Acinetobacter baumannii / drug effects Animals Anti-Bacterial Agents / chemistry Cheminformatics / methods Clostridioides difficile / drug effects Databases, Chemical Drug Discovery / methods Machine Learning Mice Mice, Inbred BALB C Mice, Inbred C57BL Mycobacterium tuberculosis / drug effects Small Molecule Libraries / chemistry Thiadiazoles / chemistry
antibiotic resistance antibiotic tolerance antibiotics drug discovery machine learning

Journal

Cell
ISSN: 1097-4172
Titre abrégé: Cell
Pays: United States
ID NLM: 0413066

Informations de publication

Date de publication:
20 02 2020
Historique:
received: 27 08 2019
revised: 04 12 2019
accepted: 15 01 2020
entrez: 22 2 2020
pubmed: 23 2 2020
medline: 25 8 2020
Statut: ppublish

Résumé

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.

Identifiants

DOI: 10.1016/j.cell.2020.01.021 PMID: 32084340 PMC: PMC8349178
pubmed: 32084340
pii: S0092-8674(20)30102-1
doi: 10.1016/j.cell.2020.01.021
pmc: PMC8349178
mid: NIHMS1723185
pii:
doi:

Substances chimiques

Anti-Bacterial Agents 0
SU 3327 0
Small Molecule Libraries 0
Thiadiazoles 0

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

688-702.e13

Subventions

Organisme : NIH HHS
ID : DP5 OD024590
Pays : United States
Organisme : CIHR
ID : FRN 143215
Pays : Canada

Commentaires et corrections

Type : ErratumIn
Type : CommentIn

Informations de copyright

Copyright © 2020 Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Interests J.J.C. is scientific co-founder and SAB chair of EnBiotix, an antibiotic drug discovery company.

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Auteurs

Jonathan M Stokes (JM)

Department of Biological Engineering, Synthetic Biology Center, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Machine Learning for Pharmaceutical Discovery and Synthesis Consortium, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Kevin Yang (K)

Machine Learning for Pharmaceutical Discovery and Synthesis Consortium, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Kyle Swanson (K)

Machine Learning for Pharmaceutical Discovery and Synthesis Consortium, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Wengong Jin (W)

Machine Learning for Pharmaceutical Discovery and Synthesis Consortium, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Andres Cubillos-Ruiz (A)

Department of Biological Engineering, Synthetic Biology Center, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA.

Nina M Donghia (NM)

Department of Biological Engineering, Synthetic Biology Center, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA.

Craig R MacNair (CR)

Department of Biochemistry and Biomedical Sciences, Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON L8N 3Z5, Canada.

Shawn French (S)

Department of Biochemistry and Biomedical Sciences, Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON L8N 3Z5, Canada.

Lindsey A Carfrae (LA)

Department of Biochemistry and Biomedical Sciences, Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON L8N 3Z5, Canada.

Zohar Bloom-Ackermann (Z)

Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.

Victoria M Tran (VM)

Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.

Anush Chiappino-Pepe (A)

Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.

Ahmed H Badran (AH)

Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.

Ian W Andrews (IW)

Department of Biological Engineering, Synthetic Biology Center, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA.

Emma J Chory (EJ)

Department of Biological Engineering, Synthetic Biology Center, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.

George M Church (GM)

Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA; Harvard-MIT Program in Health Sciences and Technology, Cambridge, MA 02139, USA.

Eric D Brown (ED)

Department of Biochemistry and Biomedical Sciences, Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON L8N 3Z5, Canada.

Tommi S Jaakkola (TS)

Machine Learning for Pharmaceutical Discovery and Synthesis Consortium, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Regina Barzilay (R)

Machine Learning for Pharmaceutical Discovery and Synthesis Consortium, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Abdul Latif Jameel Clinic for Machine Learning in Health, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. Electronic address: regina@csail.mit.edu.

James J Collins (JJ)

Department of Biological Engineering, Synthetic Biology Center, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA; Harvard-MIT Program in Health Sciences and Technology, Cambridge, MA 02139, USA; Abdul Latif Jameel Clinic for Machine Learning in Health, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. Electronic address: jimjc@mit.edu.

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Classifications MeSH

questionsmedicales.fr › Bactéries › Proteobacteria › Gammaproteobacteria › Moraxellaceae › Acinetobacter › Acinetobacter baumannii › A Deep Learning Approach to Antibiotic Discovery.
questionsmedicales.fr › Eucaryotes › Animaux › A Deep Learning Approach to Antibiotic Discovery.
questionsmedicales.fr › Actions chimiques et utilisations › Actions pharmacologiques › Utilisations thérapeutiques › Anti-infectieux › Antibactériens › A Deep Learning Approach to Antibiotic Discovery.
questionsmedicales.fr › Sciences de l'information › Informatique › Chimio-informatique › A Deep Learning Approach to Antibiotic Discovery.
questionsmedicales.fr › Bactéries › Firmicutes › Clostridiales › Clostridioides › Clostridium difficile › A Deep Learning Approach to Antibiotic Discovery.
questionsmedicales.fr › Sciences de l'information › Mémorisation et recherche des informations › Bases de données comme sujet › Bases de données factuelles › Bases de données chimiques › A Deep Learning Approach to Antibiotic Discovery.
questionsmedicales.fr › Techniques d'investigation › Développement de médicament › Découverte de médicament › A Deep Learning Approach to Antibiotic Discovery.
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Intelligence artificielle › Apprentissage machine › A Deep Learning Approach to Antibiotic Discovery.
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Rodentia › Muridae › Murinae › Souris › A Deep Learning Approach to Antibiotic Discovery.
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Rodentia › Muridae › Murinae › Souris › Lignées consanguines de souris › Souris de lignée BALB C › A Deep Learning Approach to Antibiotic Discovery.
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Rodentia › Muridae › Murinae › Souris › Lignées consanguines de souris › Souris de lignée C57BL › A Deep Learning Approach to Antibiotic Discovery.
questionsmedicales.fr › Bactéries › Bactéries à Gram positif › Bâtonnets à Gram positif › Bâtonnets asporogènes à Gram positif › Bâtonnets asporogènes réguliers à Gram positif › Mycobacteriaceae › Mycobacterium › Mycobacterium tuberculosis › A Deep Learning Approach to Antibiotic Discovery.
questionsmedicales.fr › Actions chimiques et utilisations › Utilisations spécialisées de produits chimiques › Produits chimiques de laboratoire › Bibliothèques de petites molécules › A Deep Learning Approach to Antibiotic Discovery.
questionsmedicales.fr › Composés hétérocycliques › Composés hétéromonocycliques › Azoles › Thiazoles › Thiadiazoles › A Deep Learning Approach to Antibiotic Discovery.