Network Pharmacology in Food-Medicine Homology: AI-Driven Decoding of Multi-Target Synergy from Molecular Networks to Precision Health

Network pharmacology provides a transformative framework for decoding multi-target, system-level mechanisms of food-medicine homology (FMH) substances, overcoming the limitations of reductionist approaches by integrating multi-omics data, computational modeling, and network analysis. Central to this paradigm is the “Network Targets” theory, which conceptualizes therapeutic intervention as the reconfiguration of disease-associated biological networks rather than single-target modulation. Artificial intelligence accelerates this process by enabling high-dimensional data integration, predictive modeling of synergistic combinations, and the identification of active constituents. This review outlines the key databases and computational tools that operationalize network pharmacology in FMH research and systematically categorizes their applications, including material screening, ingredient identification, synergy analysis, quality standard establishment, safety assessment, formula optimization, functional food discovery, and personalized recommendation, supported by experimental validation across numerous FMH items. Despite the challenges in data standardization and dynamic modeling, the integration of multi-omics, dynamic networks, and centralized repositories will further advance the field. Ultimately, network pharmacology will bridge traditional FMH wisdom with contemporary mechanistic rigor, positioning FMH as the cornerstone of precision nutrition and preventive medicine. Graphical Abstract: http://links.lww.com/AHM/A223