Traditional Chinese medicine (TCM), a holistic medical system rooted in dialectical theories and natural product-based therapies, has served as a cornerstone of healthcare systems for millennia. While its empirical efficacy is widely recognized, the polypharmacological mechanisms stemming from its multi-component nature remain poorly characterized. The conventional trial-and-error approaches for bioactive compound screening from herbs raise sustainability concerns, including excessive resource consumption and suboptimal temporal efficiency. The integration of artificial intelligence (AI) and multi-omics technologies with network pharmacology (NP) has emerged as a transformative methodology aligned with TCM’s inherent “multi-component, multi-target, multi-pathway” therapeutic characteristics. This convergent review provides a computational framework to decode complex bioactive compound–target–pathway networks through two synergistic strategies, (i) NP-driven dynamics interaction network modeling and (ii) AI-enhanced multi-omics data mining, thereby accelerating drug discovery and reducing experimental costs. Our analysis of 7288 publications systematically maps NP-AI–omics integration workflows for natural product screening. The proposed framework enables sustainable drug discovery through data-driven compound prioritization, systematic repurposing of herbal formulations via mechanism-based validation, and the development of evidence-based novel TCM prescriptions. This paradigm bridges empirical TCM knowledge with mechanism-driven precision medicine, offering a theoretical basis for reconciling traditional medicine with modern pharmaceutical innovation.

This study investigated the molecular mechanisms underlying the therapeutic effects of ginsenoside Rh2 (G-Rh2) in coronary heart disease (CHD) through a network pharmacology approach, focusing on identifying key targets and pathways, including those involved in lipid metabolism, metabolism regulation and anti-apoptotic signaling. A multi-target network pharmacology analysis was performed to predict the pharmacoloigical targets and pathways of G-Rh2. Key molecular interactions were validated by molecular docking. In vivo experiments using CHD rat models were conducted to verify and quantify the effects of G-Rh2 on lipid profiles, myocardial pathology, and gut microbiota composition. G-Rh2 significantly ameliorated CHD in rats by reducing serum cholesterol and triglycerides levels, alleviating myocardial fibrosis, suppressing cardiomyocyte apoptosis, and mitigating tissue damage. Mechanistically, G-Rh2 activated the PI3K/AKT signaling pathway, regulated atherosclerosis-associated metabolic pathways (e.g., pentose phosphate and carbon metabolism), and modulated gut microbiota composition by reducing the abundance of harmful bacteria and increasing beneficial microbial populations, thereby enhancing lipid metabolism and energy balance. This study demonstrates that G-Rh2 alleviates CHD through the synergistic activation of the PI3K/AKT pathway, modulation of key metabolic pathways, and restructuring of gut microbiota. These findings underscore the potential of G-Rh2 as a multi-target therapeutic agent for CHD, offering mechanistic insights into its cardioprotective properties and supporting the broader application of G-Rh2 in cardiovascular drug development.