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Artificial intelligence identifies new small-molecule senolytics

We used graph neural networks trained on experimental data to identify senolytic compounds from vast chemical libraries of over 800,000 compounds and discovered structurally diverse senolytics that have potent in vitro and in vivo activity, as well as favorable medicinal chemistry properties.

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Fig. 1: Discovering senolytics with graph neural networks.


  1. Kirkland, J. L. & Tchkonia, T. Senolytic drugs: from discovery to translation. J. Intern. Med. 288, 518–536 (2020). This review presents an overview of previously discovered senolytics and their therapeutic potential.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Wakita, M. et al. A BET family protein degrader provokes senolysis by targeting NHEJ and autophagy in senescent cells. Nat. Commun. 11, 1935 (2020). This paper shows that a BET family protein degrader is senolytic and eliminates chemotherapy-induced senescent cells in mice, increasing the efficacy of chemotherapy.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Stokes, J. M. et al. A deep learning approach to antibiotic discovery. Cell 180, 688–702.e13 (2020). This paper demonstrates that graph neural networks can be applied to discover small molecules with antibacterial activity.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Irwin, J. J. et al. ZINC20—A free ultralarge-scale chemical database for ligand discovery. J. Chem. Inf. Model. 60, 6065–6073 (2020). This paper introduces ZINC20, the latest version of an influential small molecule database, which contains about 2 billion compounds.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Rockey, D. C., Bell, P. D. & Hill, J. A. Fibrosis—a common pathway to organ injury and failure. N. Engl. J. Med. 372, 1138–1149 (2015). This review discusses the biology that underlies fibrosis.

    Article  CAS  PubMed  Google Scholar 

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This is a summary of: Wong, F., Omori, S., Donghia, N. M., Zheng, E. J. & Collins, J. J. Discovering small-molecule senolytics with deep neural networks. Nat. Aging (2023).

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Artificial intelligence identifies new small-molecule senolytics. Nat Aging 3, 640–641 (2023).

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