Decoding herbal medicine: AI-powered omics and network pharmacology
1School of Pharmacy, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China; Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, Hunan 410013, China; Zhejiang Provincial Key Laboratory of Anti-Cancer Chinese Medicines and Natural Medicines, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China; Department of Pharmacy, Hunan University of Medicine General Hospital, Huaihua, Hunan 418000, China.
Background:
As global health challenges continue to evolve, herbal medicines (HMs) have garnered significant scientific interest as a valuable resource for treating complex diseases. However, the chemical complexity of HMs presents considerable challenges in their application and research.
Purpose:
This review aims to provide a comprehensive analysis of the latest applications and technological advancements in artificial intelligence (AI), life omics, and network pharmacology within HMs research. It explores the historical and current role of HMs in managing complex diseases, with an emphasis on the integration of interdisciplinary technologies to overcome existing challenges.
Methods:
Relevant electronic databases were searched from January 2008 to December 2024, using keywords such as "Herbal Medicines", "Artificial intelligence", "Network pharmacology", and "life omics". Databases searched include Google Scholar, PubMed, Web of Science, China National Knowledge Infrastructure, and WANFANG DATA. The review followed PRISMA guidelines.
Results:
The analysis highlights the significant role of AI in characterizing functional compounds, analyzing pharmacological mechanisms, and evaluating toxicological profiles of HMs. Life omics, including genomics, transcriptomics, metabolomics, and proteomics have been crucial in analyzing active herbal components, elucidating metabolic processes, and deciphering biological networks. Additionally, the development of network pharmacology offers new approaches to establishing drug-target-disease networks and predicting pharmacological effects.
Conclusion:
The integration of interdisciplinary technologies has significantly advanced HMs research, enabling a deeper understanding of their pharmacological effects and safety profiles. However, the underlying mechanisms of HMs remain largely unclear, which has prompted the development and application of these advanced technologies. Future research should focus on further interdisciplinary collaboration to unravel these mechanisms, address challenges in evaluating drug-likeness, and ensure the effective modernization of HMs. This paper provides theoretical support and practical guidance for advancing HMs research and its application in modern healthcare.
