Autonomous AI Agents Discover Aging Interventions from Millions of Molecular Profiles

Ying, K., Tyshkovskiy, A., Moldakozhayev, A., Wang, H., De Magalhaes, C. G., Iqbal, S., Garza, A. E., Tskhay, A., Poganik, J. R., Huang, K., Qu, Y., Glubokov, D., Jin, C., Lee, D., Liu, H., Leote, C., Trapp, A., de Lima Camillo, L. P., Kerepesi, C., Moqri, M., Zhang, O., Jiang, K., Galkin, F., Zhavoronkov, A., Van Raamsdonk, J. M., Wang, M., Cong, L., Regev, A., Leskovec, J., Wyss-Coray, T., Gladyshev, V. N.

Brigham and Womens Hospital and Harvard Medical SchoolBrigham and Womens Hospital and Harvard Medical School · biorxiv

Paper

Abstract

Decades of publicly available molecular studies have generated millions of samples testing diverse interventions, yet these datasets were rarely analyzed for their effects on aging. Aging clocks now enable biological age estimation and life outcome prediction from molecular data, creating an opportunity to systematically mine this untapped resource. We developed ClockBase Agent (www.clockbase.org), a publicly accessible platform that reanalyzes millions of human and mouse methylation and RNA-seq samples by integrating them with over 40 aging clock predictions. ClockBase Agent employs specialized AI agents that autonomously generate aging-focused hypotheses, evaluate intervention effects on biological age, conduct literature reviews, and produce scientific reports across all datasets. Reanalyzing 43,602 intervention-control comparisons through multiple aging biomarkers revealed thousands of age-modifying effects missed by original investigators, including over 500 interventions that significantly reduce biological age (e.g., ouabain, KMO inhibitor, fenofibrate, and NF1 knockout). Large-scale systematic analysis reveals fundamental patterns: significantly more interventions accelerate rather than decelerate aging, disease states predominantly accelerate biological age, and loss-of-function genetic approaches systematically outperform gain-of-function strategies in decelerating aging. As validation, we show that identified interventions converge on canonical longevity pathways and with strong concordance to independent lifespan databases. We further experimentally validated ouabain, a top-scoring AI-identified candidate, demonstrating reduced frailty progression, decreased neuroinflammation, and improved cardiac function in aged mice. ClockBase Agent establishes a paradigm where specialized AI agents systematically reanalyze all prior research to identify age-modifying interventions autonomously, transforming how we extract biological insights from existing data to advance human healthspan and longevity.

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Research summary

The paper claims that specialized AI agents can autonomously identify and validate interventions that significantly reduce biological age. This research is relevant as it addresses the root causes of aging by systematically analyzing existing molecular data to discover interventions that may extend healthspan and lifespan.

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