AI algorithms find drugs that could combat ageing

A trio of chemicals that target faulty cells linked to a range of age-related conditions were found using the pioneering method, which is hundreds of times cheaper than standard screening methods, researchers say.

Findings suggest the drugs can safely remove defective cells – known as senescent cells – linked to conditions including cancer, Alzheimer’s disease and declines in eyesight and mobility.

While previous studies have shown early promise, until now few chemicals that can safely eliminate senescent cells have been identified.

These senolytic drugs are often highly toxic against normal, healthy cells in the body, researchers say.

Now, a team led by Edinburgh researchers - including from Edinburgh Cancer Research and the Cross-Disciplinary Fellowships (XDF) Programme -  has devised a way of discovering senolytic drugs using AI.

They developed a machine learning model by training it to recognise the key features of chemicals with senolytic activity, using data from more than 2,500 chemical structures mined from previous studies.

The team then used the models to screen more than 4,000 chemicals, identifying 21 potential drug candidates for experimental testing.

This study demonstrates that AI can be incredibly effective in helping us identify new drug candidates, particularly at early stages of drug discovery and for diseases with complex biology or few known molecular targets.

Dr Diego Oyarzún

Lab tests in human cells revealed that three of the chemicals – called ginkgetin, periplocin and oleandrin – were able to remove senescent cells without damaging healthy cells.

All three are natural products found in traditional herbal medicines, the team says. Oleandrin was found to be more effective than the best-performing known senolytic drug of its kind.

This work was borne out of intensive collaboration between data scientists, chemists and biologists. Harnessing the strengths of this interdisciplinary mix, we were able to build robust models and save screening costs by using only published data for model training. I hope this work will open new opportunities to accelerate the application of this exciting technology.

Dr Vanessa Smer-Barreto

The study, published in the journal Nature Communications, was supported by the Medical Research Council, Cancer Research UK, United Kingdom Research and Innovation (UKRI) and the Spanish National Research Council.

It also involved researchers from the University of Cantabria, Spain, and the Alan Turing Institute.

The study is the latest development in computer science and AI since the University established its first research hubs in the disciplines 60 years ago.

A year-long programme of events will mark achievements over the past six decades and look to the future of computer science and AI at Edinburgh.

More information about the 60 year celebration: edin.ac/60-years-computer-science-ai

Related links

• 'Discovery of senolytics using machine learning', published in Nature Communications
• Edinburgh Cancer Research website
• Cross-Disciplinary Fellowships website, recruiting now

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