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Network pharmacology in food-medicine homology: AI-driven decoding of multi-target synergy from molecular networks to precision health
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Deyang Sun1, 2, Pan Chen3, Li Tao4, Peng Ma1, Lichong Meng5, Shuting Yin5, Bo Zhang1, *, Shao Li1, *
Acupuncture and Herbal Medicine | 2026, 6(1) : 10 - 27
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Acupuncture and Herbal Medicine | 2026, 6(1): 10-27
Review Article
Network pharmacology in food-medicine homology: AI-driven decoding of multi-target synergy from molecular networks to precision health
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Deyang Sun1, 2, Pan Chen3, Li Tao4, Peng Ma1, Lichong Meng5, Shuting Yin5, Bo Zhang1, *, Shao Li1, *
Affiliations
  • 1Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRist, Department of Automation, Tsinghua University, Beijing, China
  • 2The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China
  • 3The First Clinical Medical College, Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine Prevention and Treatment of Tumor, Nanjing University of Chinese Medicine, Nanjing, China
  • 4The Key Laboratory of Syndrome Differentiation and Treatment of Gastric Cancer of the State Administration of Traditional Chinese Medicine, College of Medicine, Yangzhou University, Yangzhou, China
  • 5School of Traditional Chinese Medicine, Hunan University of Chinese Medicine, Changsha, China
Published: 2026-03-25 doi: 10.1097/HM9.0000000000000192
Outline
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Network pharmacology provides a transformative framework for decoding multi-target, system-level mechanisms of the 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 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.

Artificial intelligence  /  Food-medicine homology  /  Network pharmacology  /  Network targets
Deyang Sun, Pan Chen, Li Tao, Peng Ma, Lichong Meng, Shuting Yin, Bo Zhang, Shao Li. Network pharmacology in food-medicine homology: AI-driven decoding of multi-target synergy from molecular networks to precision health[J]. Acupuncture and Herbal Medicine, 2026 , 6 (1) : 10 -27 . DOI: 10.1097/HM9.0000000000000192
  • project of Henan-Zhongjing Pharmaceutical Big Data Repository and Large Model Algorithm Development Research(252028037)
Year 2026 volume 6 Issue 1
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Article Info
doi: 10.1097/HM9.0000000000000192
  • Receive Date:2025-11-25
  • Online Date:2026-06-05
  • Published:2026-03-25
Article Data
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History
  • Received:2025-11-25
  • Accepted:2026-02-12
Funding
project of Henan-Zhongjing Pharmaceutical Big Data Repository and Large Model Algorithm Development Research(252028037)
Affiliations
    1Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRist, Department of Automation, Tsinghua University, Beijing, China
    2The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China
    3The First Clinical Medical College, Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine Prevention and Treatment of Tumor, Nanjing University of Chinese Medicine, Nanjing, China
    4The Key Laboratory of Syndrome Differentiation and Treatment of Gastric Cancer of the State Administration of Traditional Chinese Medicine, College of Medicine, Yangzhou University, Yangzhou, China
    5School of Traditional Chinese Medicine, Hunan University of Chinese Medicine, Changsha, China

Corresponding:

* Corresponding author. Bo Zhang, E-mail:
Shao Li, E-mail: .
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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