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  3. Discovering small-molecule senolytics with deep neural networks

Discovering small-molecule senolytics with deep neural networks

  • Nat Aging. 2023 Jun;3(6):734-750. doi: 10.1038/s43587-023-00415-z.
Felix Wong # 1 2 3,  Satotaka Omori # 2 3 4,  Nina M Donghia 1 5,  Erica J Zheng 2 6,  James J Collins 7 8 9
Affiliations

Affiliations

  • 1 Institute for Medical Engineering & Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • 2 Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • 3 Integrated Biosciences, Inc, San Carlos, CA, USA.
  • 4 Division of Cancer Cell Biology, Institute of Medical Science, The University of Tokyo, Minato-Ku, Tokyo, Japan.
  • 5 Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA.
  • 6 Program in Chemical Biology, Harvard University, Cambridge, MA, USA.
  • 7 Institute for Medical Engineering & Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. jimjc@mit.edu.
  • 8 Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA. jimjc@mit.edu.
  • 9 Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA. jimjc@mit.edu.
  • # Contributed equally.
PMID: 37142829 DOI: 10.1038/s43587-023-00415-z
Abstract

The accumulation of senescent cells is associated with aging, inflammation and cellular dysfunction. Senolytic drugs can alleviate age-related comorbidities by selectively killing senescent cells. Here we screened 2,352 compounds for senolytic activity in a model of etoposide-induced senescence and trained graph neural networks to predict the senolytic activities of >800,000 molecules. Our approach enriched for structurally diverse compounds with senolytic activity; of these, three drug-like compounds selectively target senescent cells across different senescence models, with more favorable medicinal chemistry properties than, and selectivity comparable to, those of a known senolytic, ABT-737. Molecular docking simulations of compound binding to several senolytic protein targets, combined with time-resolved fluorescence energy transfer experiments, indicate that these compounds act in part by inhibiting Bcl-2, a regulator of cellular Apoptosis. We tested one compound, BRD-K56819078, in aged mice and found that it significantly decreased senescent cell burden and mRNA expression of senescence-associated genes in the kidneys. Our findings underscore the promise of leveraging deep learning to discover senotherapeutics.

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