Mechanism-informed, AI-driven frameworks for discovery and validation of aging-associated chemical space

Abstract

Aging is a progressive, multifactorial biological process that drives the risk of nearly all major chronic diseases, including cancer, neurodegeneration, metabolic disorders, frailty, and cardiovascular dysfunction. Although the past two decades have established a molecular framework through the Hallmarks of Aging, translating this knowledge into actionable, small-molecule interventions that enhance healthspan remains a central challenge in the field of geroscience. Experimental discovery pipelines are slow, resource-intensive, and typically explore only a minute fraction of chemical space. Conversely, computational drug discovery approaches, while high-throughput, often rely on chemistry-centric descriptors, exhibit black-box behavior, lack mechanistic interpretability, and rarely generalize to biologically novel molecules. This thesis addresses these long-standing limitations by developing two complementary artificial intelligence systems, AgeXtend and AgeXtend:: Mimetics, designed to accelerate mechanism-informed discovery of geroprotective molecules and caloric restriction mimetics (CRMs). The first objective introduces AgeXtend, a multimodal, bioactivity-driven, and fully explainable AI framework. AgeXtend integrates curated datasets of experimentally validated geroprotectors and neutral compounds with bioactivity-based descriptors, hallmark-specific classification models, toxicity prediction, and target inference modules. By combining mechanistic knowledge with machine learning, AgeXtend achieves robust predictive accuracy across cross-validation, leave-one-out validation, and independent external datasets. Importantly, the explainability module maps predictions onto nine aging pathways, allowing for a mechanistic interpretation of each compound’s mode of action. Large-scale screening of ~1.1 billion compounds yielded diverse chemical classes with strong geroprotective potential. Experimental validation confirmed these predictions across three biological systems: Saccharomyces cerevisiae chronological lifespan assays, human fibroblast senescence assays, and Celegans lifespan assays. Endogenous metabolites and repurposed drugs predicted by AgeXtend demonstrated lifespan-extending or senomodulatory activity, underscoring the biological fidelity of its predictions. Building upon this foundation, the second objective presents AgeXtend::Mimetics, a novel computational framework designed to identify Caloric Restriction Mimetics, compounds capable of reproducing CR-like physiological responses without structural similarity to known CRMs. Unlike existing approaches that rely on transcriptomic signatures alone or structural matching, AgeXtend::Mimetics explicitly decouples biological convergence from chemical divergence. Using dual similarity modeling, ridge regression residuals, supervised contrastive learning, and composite CRM fingerprinting, the framework identifies molecules whose biological signatures align strongly with known CRMs despite having distinct chemical architectures. Large-scale application across thousands of compounds revealed chemically novel, mechanistically plausible CRM candidates that align with pathways such as nutrient sensing, autophagy, mitochondrial remodeling, and metabolic regulation. This framework substantially broadens the chemical landscape of CRM discovery and provides mechanistic clarity on CRM-like effects. Together, the approaches developed in this thesis demonstrate that explainable, mechanism-oriented AI models can successfully bridge the gap between large-scale chemical exploration and biological relevance. AgeXtend and AgeXtend::Mimetics collectively advance the field of computational geroscience by enabling scalable, interpretable, and experimentally validated discovery of geroprotectors and CRMs. These contributions lay the groundwork for future translational studies, the development of generative design for longevity therapeutics, and the integration of multi-omic datasets to refine mechanism-based discovery pipelines. The thesis highlights both the promise and current limitations of AI in aging biology, providing a roadmap for next-generation computational frameworks that target healthspan extension.