AI-Based Drug Discovery Helps Identify Senolytic Compounds

A collaborative research team recently published a study in Nature Aging demonstrating the power of artificial intelligence (AI) in discovering novel senolytic compounds. Senolytics are small molecules studied for their ability to suppress age-related processes such as fibrosis, inflammation, and cancer. 

The researchers used AI-guided screening of more than 800,000 compounds to reveal three drug candidates with comparable efficacy and superior medicinal chemistry properties to those of senolytics currently under investigation. Senolytics are compounds that selectively induce apoptosis, or programmed cell death, in senescent cells that are no longer dividing. A hallmark of aging, senescent cells have been implicated in various age-related diseases and conditions, including cancer, diabetes, cardiovascular disease, and Alzheimer’s disease.

The researchers trained deep neural networks on experimentally generated data to predict the senolytic activity of any molecule. Using this AI model, they discovered three highly selective and potent senolytic compounds from a chemical space of over 800,000 molecules. All three displayed chemical properties suggestive of high oral bioavailability and were found to have favorable toxicity profiles in hemolysis and genotoxicity tests.

Structural and biochemical analyses indicate that all three compounds bind Bcl-2, a protein that regulates apoptosis and is also a chemotherapy target. Experiments testing one of the compounds in 80-week-old mice, roughly corresponding to 80-year-old humans, found that it cleared senescent cells and reduced expression of senescence-associated genes in the kidneys.

“This research result is a significant milestone for both longevity research and the application of artificial intelligence to drug discovery,” said Felix Wong, Ph.D., co-founder of Integrated Biosciences and first author of the publication. “These data demonstrate that we can explore chemical space in silico and emerge with multiple candidate anti-aging compounds that are more likely to succeed in the clinic, compared to even the most promising examples of their kind being studied today.”

These findings demonstrate how AI applications in drug discovery can lead to identifying novel senolytic compounds with superior medicinal chemistry properties. These compounds may have improved prospects in clinical trials and could eventually help restore health to aging individuals.