Identifying Herbal Candidates and Active Ingredients Against Postmenopausal Osteoporosis Using Biased Random Walk on a Multiscale Network

Discovery of Herbal Remedies and Key Components for Major Depressive Disorder Through Biased Random Walk Analysis on a Multiscale Network

Major depressive disorder (MDD) is a widespread psychiatric condition with substantial socioeconomic impacts, yet single-target pharmacotherapies often yield responses. To address its multifactorial nature, this study employed a multiscale network analysis of herbs, their active components, and MDD-associated protein targets. Using a biased random walk with restart, we calculated interactions between disease-related and herb-derived targets, identifying herbs highly correlated with MDD. Enrichment analysis further revealed key signaling pathways, including oxidative stress, neuroinflammation, and hormone metabolism, underlying these herbs' therapeutic effects. We identified Ephedrae herba, Glehniae radix, Euryales semen, and Campsitis flos as promising candidates, each containing multiple bioactive compounds (such as ephedrine, psoralen, xanthine, and ursolic acid) that modulate critical processes like oxidation-reduction, inflammatory cytokine regulation, and transcriptional control. Network visualization showed how these herbs collectively target both shared and distinct pathways, supporting a synergistic, multi-target therapeutic strategy. This approach underscores the significance of network-based methodologies in addressing complex disorders such as MDD, where focusing on a single target may overlook synergistic interactions. By integrating diverse molecular data, this study provides a systematic framework for identifying novel interventions. Future experimental validation will be crucial to confirm these predictions and facilitate the translation of findings into effective MDD therapies.