ABSTRACT
Cellular senescence is a stress response characterised by a permanent cell cycle arrest and a proinflammatory secretome. In addition to its tumour suppressor role, senescence is involved in ageing and promotes many disease processes such as cancer, type 2 diabetes, osteoarthritis, and SARS-CoV-2 infection. There is a growing interest in therapies based on targeted elimination of senescent cells, yet so far only a few such senolytics are known, partly due to the poor grasp of the molecular mechanisms that control the senescence survival programme. Here we report a highly effective machine learning pipeline for the discovery of senolytic compounds. Using solely published data, we trained machine learning algorithms to classify compounds according to their senolytic action. Models were trained on as few as 58 known senolytics against a background of FDA-approved compounds or in late-stage clinical development (2,523 in total). We computationally screened various chemical libraries and singled out top candidates for validation in human lung fibroblasts (IMR90) and lung adenocarcinoma (A549) cell lines. This led to the discovery of three novel senolytics: ginkgetin, oleandrin and periplocin, with potency comparable to current senolytics and a several hundred-fold reduction in experimental screening costs. Our work demonstrates that machine learning can take maximum advantage of existing drug screening data, paving the way for new open science approaches to drug discovery for senescence-associated diseases.