Synergistic Geroprotectors Mapping through Systems Machine Learning and Graph Neural Networks.

Abstract

Geroscience offers a transformative paradigm by targeting shared aging hallmarks to enable simultaneous modulation of multiple age-related disorders (ARDs). Yet, current geroprotective interventions often lack mechanistic breadth, as targeting isolated pathways yields limited benefits compared to interventions modulating interconnected regulators of aging biology. To bridge this gap, a systems-level strategy was designed around four key targets, including, Nrf2/Keap1, mTORC1, AMPK, and SIRT1, responsible for regulating oxidative stress, mitochondrial dysfunction, proteostasis, and autophagy. Concurrent regulation of these targets was identified to potentially induce a concerted and sustained geroprotective effect across diverse ARDs. A machine learning-based geroprotector classification model was developed to identify natural compounds capable of executing this integrated strategy. Subsequent drug-likeness screening confirmed favorable pharmacokinetic properties of the predicted compounds, while molecular docking revealed compounds with strong binding affinities with all four geroprotective targets, thereby leading to the identification of a subset of natural compounds with the potential to induce a coordinated geroprotective response. Finally, a graph neural network-based synergy prediction model, trained on known ARD drug combinations, identified five high-confidence pairs composed of four natural compounds, including Baicalein, Pectolinarigenin, Phloretin, and Demethoxycurcumin. These computationally predicted combinations hold the potential to elicit synergistic and comprehensive geroprotective effects across multiple ARDs.