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AI Helps To Find Drugs That May Counter the Effects of Aging

An outstretched hand holding an assortment of pills.
Credit: Ksenia Yakovleva / Unsplash.
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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.

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Harmful side-effects

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.

Machine learning

Now, a team led by Edinburgh researchers 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 School of Informatics and School of Biological Sciences

Natural products

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 Institute of Genetics and Cancer and School of Informatics

Reference: Smer-Barreto V, Quintanilla A, Elliott RJR, et al. Discovery of senolytics using machine learning. Nat Commun. 2023;14(1):3445. doi: 10.1038/s41467-023-39120-1

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