2. Academic Validation
  3. Discovery of senolytics using machine learning

Discovery of senolytics using machine learning

  • Nat Commun. 2023 Jun 10;14(1):3445. doi: 10.1038/s41467-023-39120-1.
Vanessa Smer-Barreto # 1,  Andrea Quintanilla # 2,  Richard J R Elliott 3,  John C Dawson 3,  Jiugeng Sun 4,  Víctor M Campa 2,  Álvaro Lorente-Macías 3,  Asier Unciti-Broceta 3,  Neil O Carragher 3,  Juan Carlos Acosta 5 6,  Diego A Oyarzún 7 8 9
Affiliations

Affiliations

  • 1 Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Cancer, University of Edinburgh, Crewe Road, Edinburgh, EH4 2XR, UK. vanessa.smerbarreto@ed.ac.uk.
  • 2 Instituto de Biomedicina y Biotecnología de Cantabria (IBBTEC), CSIC-Universidad de Cantabria-SODERCAN. C/ Albert Einstein 22, Santander, 39011, Spain.
  • 3 Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Cancer, University of Edinburgh, Crewe Road, Edinburgh, EH4 2XR, UK.
  • 4 School of Informatics, University of Edinburgh, 10 Crichton St, Edinburgh, EH8 9AB, UK.
  • 5 Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Cancer, University of Edinburgh, Crewe Road, Edinburgh, EH4 2XR, UK. juan.acosta@unican.es.
  • 6 Instituto de Biomedicina y Biotecnología de Cantabria (IBBTEC), CSIC-Universidad de Cantabria-SODERCAN. C/ Albert Einstein 22, Santander, 39011, Spain. juan.acosta@unican.es.
  • 7 School of Informatics, University of Edinburgh, 10 Crichton St, Edinburgh, EH8 9AB, UK. d.oyarzun@ed.ac.uk.
  • 8 School of Biological Sciences, University of Edinburgh, Max Born Crescent, Edinburgh, EH9 3BF, UK. d.oyarzun@ed.ac.uk.
  • 9 The Alan Turing Institute, 96 Euston Road, London, NW1 2DB, UK. d.oyarzun@ed.ac.uk.
  • # Contributed equally.
PMID: 37301862 DOI: 10.1038/s41467-023-39120-1
Abstract

Cellular senescence is a stress response involved in ageing and diverse disease processes including Cancer, type-2 diabetes, osteoarthritis and viral Infection. Despite growing interest in targeted elimination of senescent cells, only few senolytics are known due to the lack of well-characterised molecular targets. Here, we report the discovery of three senolytics using cost-effective machine learning algorithms trained solely on published data. We computationally screened various chemical libraries and validated the senolytic action of ginkgetin, periplocin and oleandrin in human cell lines under various modalities of senescence. The compounds have potency comparable to known senolytics, and we show that oleandrin has improved potency over its target as compared to best-in-class alternatives. Our approach led to several hundred-fold reduction in drug screening costs and demonstrates that artificial intelligence can take maximum advantage of small and heterogeneous drug screening data, paving the way for new open science approaches to early-stage drug discovery.

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