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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月1日

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  • Simon Hanassab,
  • Joshua Southern,
  • Ayomide V. Olabode,
  • Ivan Laponogov,
  • Michael Bronstein,
  • Alexander N. Comninos,
  • Thomas Heinis,
  • Ali Abbara,
  • Chioma Izzi-Engbeaya,
  • Kirill Veselkov,
  • Waljit S. Dhillo

摘要

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.