Researchers from the University of Edinburgh, Scotland, and the University of Cantabria, Spain, have collaborated to develop an AI trained to discover senolytic medicinal chemistry in familiar compounds.
In the paper, “Discovery of senolytics using machine learning,” published in Nature Communications, the researchers provide details on their efforts to search over 4,300 scientifically described compounds for the appropriate chemical makeup to address cellular senescence.
Cellular senescence occurs when a cell ceases to multiply. This can happen due to aging, where a cell may determine that there are too many accumulated mutations to replicate safely, or because the cell has been damaged. Senescence plays a crucial role in limiting tumor progression by preventing damaged or mutated cells from proliferating.
The body can tolerate a few non-participating senescent cells in a tissue. However, accumulating high levels of senescent cells with age is associated with various diseases such as osteoarthritis, lung disease, Alzheimer’s, dementia, and cancer.
The screening method involved training the AI using 58 previously identified senolytics and 2,465 compounds known to have no senolytic effects. After sufficient training, the AI was exposed to libraries containing 4,340 FDA-approved or clinical-stage compounds.
2023-06-17 08:30:03
Link from phys.org