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

According to scientists at the University of Edinburgh, they’ve used an AI algorithm to discover new drugs that can target and combat faulty cells linked to cancer, Alzheimer’s disease, and declines in eyesight and mobility.

The faulty cells – known as senescent cells – could be safely removed with the new drugs discovered via AI.

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

Previous senolytic drugs are often highly toxic against normal, healthy cells in the body, researchers say, making them unsuitable for treatment.

Now, the 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.

Then, the team 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 Oyazún, a researcher from the School of Informatics and School of Biological Sciences at the University of Edinburgh, said.

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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,” said Dr Vanessa Smer-Barreto, from the Institute of Genetics and Cancer and School of Informatics.

“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.”

The study, published in the journal of Nature Comunications, 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.