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Discovery of senolytics using machine learning

Medicine and Health

Discovery of senolytics using machine learning

V. Smer-barreto, A. Quintanilla, et al.

Discover the groundbreaking potential of senolytics in combating aging and diseases! This research, conducted by a team of experts including Vanessa Smer-Barreto and Andrea Quintanilla, unveils three compounds with remarkable senolytic properties, particularly highlighting oleandrin's unparalleled potency. Dive into the innovative use of AI in drug discovery that promises to revolutionize treatment options.

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Playback language: English
Abstract
Cellular senescence is a stress response involved in aging and various diseases. This paper reports the discovery of three senolytics (ginkgetin, periplocin, and oleandrin) using machine learning algorithms trained on published data. These compounds showed senolytic action in human cell lines, with oleandrin exhibiting improved potency compared to existing alternatives. This approach significantly reduced drug screening costs and demonstrates the potential of AI in early-stage drug discovery.
Publisher
Nature Communications
Published On
Jun 10, 2023
Authors
Vanessa Smer-Barreto, 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, Diego A. Oyarzún
Tags
cellular senescence
senolytics
machine learning
aging
drug discovery
oleandrin
cost reduction

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