EconPapers    
Economics at your fingertips  
 

Discovery of senolytics using machine learning

Vanessa Smer-Barreto (vanessa.smerbarreto@ed.ac.uk), Andrea Quintanilla, Richard J. R. Elliott, John C. Dawson, Jiugeng Sun, Víctor M. Campa, Álvaro Lorente-Macías, Asier Unciti-Broceta, Neil O. Carragher, Juan Carlos Acosta (juan.acosta@unican.es) and Diego A. Oyarzún (d.oyarzun@ed.ac.uk)
Additional contact information
Vanessa Smer-Barreto: University of Edinburgh
Andrea Quintanilla: CSIC-Universidad de Cantabria-SODERCAN. C/ Albert Einstein 22
Richard J. R. Elliott: University of Edinburgh
John C. Dawson: University of Edinburgh
Jiugeng Sun: University of Edinburgh
Víctor M. Campa: CSIC-Universidad de Cantabria-SODERCAN. C/ Albert Einstein 22
Álvaro Lorente-Macías: University of Edinburgh
Asier Unciti-Broceta: University of Edinburgh
Neil O. Carragher: University of Edinburgh
Juan Carlos Acosta: University of Edinburgh
Diego A. Oyarzún: University of Edinburgh

Nature Communications, 2023, vol. 14, issue 1, 1-15

Abstract: 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.

Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-023-39120-1 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-39120-1

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-023-39120-1

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla (sonal.shukla@springer.com) and Springer Nature Abstracting and Indexing (indexing@springernature.com).

 
Page updated 2024-12-28
Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-39120-1