【摘要】 网络药理学为解码药食同源物质的多靶点、协同作用机制提供了新的研究范式，它通过整合多组学数据、计算建模和网络分析，为从系统层面研究复杂体系提供了新工具。网络药理学的核心是“网络靶标”理论，该理论强调药物作用在于调控疾病相关的生物学网络，而非单一靶点的调节。人工智能通过实现高维数据整合、协同组合的预测性建模以及活性成分的识别，加速了这一过程。本文系统梳理了药食同源网络药理学研究常用的数据库与方法，从物质筛选、成分鉴定、协同机制、质量控制、安全性评价、复方优化及个性化推荐等方面综述其应用进展。尽管在数据标准化和动态网络建模方面仍面临挑战，随着多组学技术与网络模型的持续发展，网络药理学有望为连接传统药食同源理念与现代精准营养研究提供支撑。更多还原

【Abstract】 Network pharmacology provides a transformative framework for decoding multi-target, system-level mechanisms of the foodmedicine 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 the modulation of isolated single targets. 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.更多还原