AI algorithms find drugs that could combat ageing

Three drugs that could help stave off the effects of ageing have been discovered using artificial intelligence (AI), a study suggests.

A groundbreaking method has identified three chemicals that effectively target faulty cells associated with age-related conditions. These compounds, ginkgetin, periplocin, and oleandrin, can safely eliminate senescent cells linked to cancer, Alzheimer’s disease, declining eyesight, and mobility issues.

Previous studies showed promise, but few drugs capable of removing senescent cells without harming healthy cells were found. However, a team of Edinburgh researchers developed a machine learning model using AI to discover senolytic drugs. By training the model on data from over 2,500 chemical structures, they screened more than 4,000 chemicals and identified 21 potential drug candidates.

Lab tests confirmed the efficacy of ginkgetin, periplocin, and oleandrin in removing senescent cells without damaging healthy ones. Remarkably, these compounds are natural products derived from traditional herbal medicines.

This groundbreaking work, supported by esteemed organizations such as the Medical Research Council and Cancer Research UK, demonstrates the power of AI in early-stage drug discovery for complex diseases. The collaboration between data scientists, chemists, and biologists paved the way for robust models while minimizing screening costs.

Dr. Diego OyarzĂșn from the School of Informatics and School of Biological Sciences, and Dr. Vanessa Smer-Barreto from the Institute of Genetics and Cancer and School of Informatics led this research milestone. The study, published in Nature Communications, also involved researchers from the University of Cantabria, Spain, and the Alan Turing Institute.

This research marks a significant advancement in computer science and AI at the University of Edinburgh, which has been celebrating its achievements in these fields over the past sixty years. Learn more about the 60-year celebration at

Source The University of Edinburgh

Author: Neurologica

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