A Deep Learning Approach to Antibiotic Discovery
Name
CELL-D-19-02462_R3.pdf
Description
Accepted version
Size
4.55 MB
Format
Adobe PDF
Checksum (MD5)
ba7514bcfc5012d7ed95cbb6ee8120d7
Author(s)
Stokes, Jonathan
Yang, Kevin
Swanson, Kyle
Jin, Wengong
Cubillos, Andres Fernando
Donghia, Nina
MacNair, Craig R.
French, Shawn
Carfrae, Lindsey A.
Bloom-Ackermann, Zohar
Date Issued
February 2020
Journal
Cell
Publisher
Elsevier BV
Citation
Stokes, Jonathan M. et al. "A Deep Learning Approach to Antibiotic Discovery." 180, 4 (February 2020): 688-702 © 2020 Elsevier
Version
Author's final manuscript
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. A trained deep neural network predicts antibiotic activity in molecules that are structurally different from known antibiotics, among which Halicin exhibits efficacy against broad-spectrum bacterial infections in mice.
MIT Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.cell.2020.01.021
