Discovery of a structural class of antibiotics with explainable deep learning.

Journal: Nature
PMID: 38123686

Abstract

The discovery of novel structural classes of antibiotics is urgently needed to address the ongoing antibiotic resistance crisis. Deep learning approaches have aided in exploring chemical spaces; these typically use black box models and do not provide chemical insights. Here we reasoned that the chemical substructures associated with antibiotic activity learned by neural network models can be identified and used to predict structural classes of antibiotics. We tested this hypothesis by developing an explainable, substructure-based approach for the efficient, deep learning-guided exploration of chemical spaces. We determined the antibiotic activities and human cell cytotoxicity profiles of 39,312 compounds and applied ensembles of graph neural networks to predict antibiotic activity and cytotoxicity for 12,076,365 compounds. Using explainable graph algorithms, we identified substructure-based rationales for compounds with high predicted antibiotic activity and low predicted cytotoxicity. We empirically tested 283 compounds and found that compounds exhibiting antibiotic activity against Staphylococcus aureus were enriched in putative structural classes arising from rationales. Of these structural classes of compounds, one is selective against methicillin-resistant S. aureus (MRSA) and vancomycin-resistant enterococci, evades substantial resistance, and reduces bacterial titres in mouse models of MRSA skin and systemic thigh infection. Our approach enables the deep learning-guided discovery of structural classes of antibiotics and demonstrates that machine learning models in drug discovery can be explainable, providing insights into the chemical substructures that underlie selective antibiotic activity.

Authors

  • Felix Wong
    Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Erica J Zheng
    Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Jacqueline A Valeri
    Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, 02115, USA.
  • Nina M Donghia
    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.
  • Melis N Anahtar
    Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
  • Satotaka Omori
    Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Alicia Li
    Integrated Biosciences, San Carlos, CA, USA.
  • Andres Cubillos-Ruiz
    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.
  • Aarti Krishnan
    Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Wengong Jin
    Computer Science and Artificial Intelligence Laboratory , Massachusetts Institute of Technology , 77 Massachusetts Avenue , Cambridge , MA 02139 , USA . Email: regina@csail.mit.edu.
  • Abigail L Manson
    Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Jens Friedrichs
    Leibniz Institute of Polymer Research and the Max Bergmann Center of Biomaterials, Dresden, Germany.
  • Ralf Helbig
    Leibniz Institute of Polymer Research and the Max Bergmann Center of Biomaterials, Dresden, Germany.
  • Behnoush Hajian
    Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Dawid K Fiejtek
    Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Florence F Wagner
    Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Holly H Soutter
    Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Ashlee M Earl
    Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Jonathan M Stokes
    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.
  • Lars D Renner
    Leibniz Institute of Polymer Research and the Max Bergmann Center of Biomaterials, Dresden, Germany.
  • James J Collins
    Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.

Keywords

External Resources

Popular Topics
Recent Journals