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Discovering small-molecule senolytics with deep neural networks

Wong, Felix, Omori, Satotaka, Donghia, Nina M., Zheng, Erica J., Collins, James J.·2023·Nature Aging
DOI 10.1038/s43587-023-00415-z
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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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Metadata

Authors
Wong, F., Omori, S., Donghia, N. M., Zheng, E. J., & Collins, J. J.
Venue
Nature Aging
Publisher
Springer Science and Business Media LLC
Published
2023-05-04
Volume
3
Issue
6
Pages
734-750
ISSN
2662-8465
DOI
10.1038/s43587-023-00415-z
Cited by
82
References
29
Language
en
Licence
Funders
  • James S. McDonnell Foundation
  • Broad Institute of MIT and Harvard

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