Discovering geroprotectors through the explainable artificial intelligence-based platform AgeXtend

Abstract

Aging involves metabolic changes that lead to reduced cellular fitness, yet the role of many metabolites in aging is unclear. Understanding the mechanisms of known geroprotective molecules reveals insights into metabolic networks regulating aging and aids in identifying additional geroprotectors. Here we present AgeXtend, an artificial intelligence (AI)-based multimodal geroprotector prediction platform that leverages bioactivity data of known geroprotectors. AgeXtend encompasses modules that predict geroprotective potential, assess toxicity and identify target proteins and potential mechanisms. We found that AgeXtend accurately identified the pro-longevity effects of known geroprotectors excluded from training data, such as metformin and taurine. Using AgeXtend, we screened ~1.1 billion compounds and identified numerous potential geroprotectors, which we validated using yeast and Caenorhabditis elegans lifespan assays, as well as exploring microbiome-derived metabolites. Finally, we evaluated endogenous metabolites predicted as senomodulators using senescence assays in human fibroblasts, highlighting AgeXtend’s potential to reveal unidentified geroprotectors and provide insights into aging mechanisms. Arora et al. present AgeXtend, an explainable artificial intelligence-based platform that leverages bioactivity data to predict geroprotectors. They validate potential geroprotectors identified using this platform in yeast, worm and senescence assays.