57
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: not found
      • Article: not found

      A Deep Learning Approach to Antibiotic Discovery

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          <p class="first" id="d2807436e301">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 &gt;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. </p>

          Related collections

          Author and article information

          Journal
          Cell
          Cell
          Elsevier BV
          00928674
          February 2020
          February 2020
          : 180
          : 4
          : 688-702.e13
          Article
          10.1016/j.cell.2020.01.021
          8349178
          32084340
          56bab5e3-599c-450a-9bc7-14d5b3b39335
          © 2020

          https://www.elsevier.com/tdm/userlicense/1.0/

          http://www.elsevier.com/open-access/userlicense/1.0/

          History

          Comments

          Comment on this article