A multi-target computational approach to target the polycystic ovary syndrome receptors using natural compounds

Abstract Background Polycystic ovary syndrome (PCOS) is a prevalent endocrinopathy characterized by excess androgen production and associated metabolic disturbances. Although several synthetic drugs, such as hypoglycemic and ovulation-inducing agents, are used in PCOS management, their clinical outcomes remain limited due to adverse side effects. Therefore, natural compounds offer promising alternatives or adjunct drugs with improved safety and efficacy. Objective and methods This study evaluated phytochemicals from Anethum graveolens (dill) for potential therapeutic activity against PCOS using an integrative computational strategy. PCOS-related targets were identified through DiseaseNet and GeneCards, followed by protein–protein interaction (PPI) analysis via STRING and hub gene selection with CytoHubba. Lead compounds were assessed through molecular docking, density functional theory (DFT) calculations, and to confirm the stability of the compound–target interactions, we performed molecular dynamics (MD) simulations (100 ns). Results The selected phytochemicals demonstrated favorable drug-likeness properties. DFT analysis revealed strong reactivity of carvacrol, eugenol, geraniol, and thymol, indicated by low energy gaps. Docking studies showed that these compounds exhibited stronger binding affinities with key PCOS targets compared to the control drug metformin. MD simulations further confirmed the stable interaction of these compounds with PCOS-associated proteins, including MMP9, AR, ALB, and PPAR-α. Conclusion Carvacrol, eugenol, geraniol, and thymol exhibited strong binding affinities, favorable drug-likeness profiles, and stable interactions with PCOS-associated receptors, highlighting their potential as multi-target therapeutic candidates for PCOS management.