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August 31, 2021 Artificial intelligence (AI) has been paired with one of the simplest of organisms--the nematode Caenorhabditis elegans--to enlighten the scientific community about the physical and chemical properties of drug compounds with anti-aging effects, according to Brendan Howlin, reader in computational chemistry at the University of Surrey (U.K.). The predictive power of the methodology has just been demonstrated using an established database of small molecules found to extend life in model organisms. The 1,738 compounds in the DrugAge database were broadly separated into flavonoids (e.g., from fruits and vegetables), fatty acids (e.g, omega-3 fatty acids), and those with a carbon-oxygen bond (e.g., alcohol)--all heavily tied to nutrition and lifestyle choices. Pharmaceuticals could be developed based on that nutraceutical knowledge, including the importance of the number of nitrogen atoms, says Howlin. Unlike prior efforts using AI to identify compounds that slow the aging process, Howlin used machine learning to calculate the quantitative structure–activity relationship (QSAR) of molecules.