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A Deep Learning Approach to Antibiotic Discovery.
Stokes, Jonathan M; Yang, Kevin; Swanson, Kyle; Jin, Wengong; Cubillos-Ruiz, Andres; Donghia, Nina M; MacNair, Craig R; French, Shawn; Carfrae, Lindsey A; Bloom-Ackermann, Zohar; Tran, Victoria M; Chiappino-Pepe, Anush; Badran, Ahmed H; Andrews, Ian W; Chory, Emma J; Church, George M; Brown, Eric D; Jaakkola, Tommi S; Barzilay, Regina; Collins, James J.
Afiliación
  • 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 Co
  • 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.
  • 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.
  • 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.
  • 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
  • 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.
  • MacNair CR; Department of Biochemistry and Biomedical Sciences, Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON L8N 3Z5, Canada.
  • French S; Department of Biochemistry and Biomedical Sciences, Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON L8N 3Z5, Canada.
  • Carfrae LA; Department of Biochemistry and Biomedical Sciences, Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON L8N 3Z5, Canada.
  • Bloom-Ackermann Z; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.
  • Tran VM; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
  • 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.
  • Badran AH; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
  • 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
  • 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.
  • 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.
  • Brown ED; Department of Biochemistry and Biomedical Sciences, Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON L8N 3Z5, Canada.
  • 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.
  • 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 Lea
  • 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
Cell ; 180(4): 688-702.e13, 2020 02 20.
Article en En | MEDLINE | ID: mdl-32084340
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Tiadiazoles / Descubrimiento de Drogas / Aprendizaje Automático / Antibacterianos Tipo de estudio: Prognostic_studies Límite: Animals Idioma: En Revista: Cell Año: 2020 Tipo del documento: Article
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Tiadiazoles / Descubrimiento de Drogas / Aprendizaje Automático / Antibacterianos Tipo de estudio: Prognostic_studies Límite: Animals Idioma: En Revista: Cell Año: 2020 Tipo del documento: Article