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This article reports the development and validation of a machine learning pipeline for the discovery of senolytics. A dataset was mined from multiple sources and used to train machine learning models predictive of senolytic action. A library of more than 4000 compounds was computationally screened and a reduced set of 21 candidate hits were identified for experimental validation. Three compounds were found to have senolytic activity: ginkgetin, oleandrin and periplocin. This work demonstrates that machine learning can take maximum advantage of published screening data to find new active therapeutic compounds.