Identifying nutraceutical targets to treat polycystic ovary syndrome using graph representation learning.

ABSTRACT: Polycystic ovary syndrome (PCOS) is a complex, multifactorial, and polygenic disorder. Here, we employed machine learning (ML) techniques to analyze large open-source datasets to identify bioactive molecules in foods and pharmacological agents that interact with genes and biological functions central to PCOS pathophysiology. We selected 13 PCOS-associated genes as targets, and the network propagation algorithm systematically identified bioactive molecules that interact with pathways relevant to PCOS. Among the top-ranked molecules, epicatechin-3-gallate (found in green tea) and 24-methylenecycloartan-3-ol (found in almonds) were newly identified, with green tea and almonds previously demonstrated to have anti-androgenic and anti-inflammatory properties. Validation of the ML pipeline with clinically available drugs revealed significant interactions with gonadotropin-releasing hormone receptor modulators, consistent with their established role in PCOS pathophysiology. These findings identify novel therapeutic targets for further research in precision nutrition and drug repurposing for PCOS treatment.

SUBMITTER: Hanassab S 

PROVIDER: S-EPMC12669042 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature