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Exploring artificial intelligence approaches for predicting synergistic effects of active compounds in traditional Chinese medicine based on molecular compatibility theory 被引量:1
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作者 Yiwen Wang Tong Wu +5 位作者 Xingyu Li Qilan Xu Heshui Yu Shixin Cen Yi Wang Zheng Li 《Chinese Journal of Natural Medicines》 2025年第11期1409-1424,共16页
Due to its synergistic effects and reduced side effects,combination therapy has become an important strategy for treating complex diseases.In traditional Chinese medicine(TCM),the“monarch,minister,assistant,envoy”co... Due to its synergistic effects and reduced side effects,combination therapy has become an important strategy for treating complex diseases.In traditional Chinese medicine(TCM),the“monarch,minister,assistant,envoy”compatibilities theory provides a systematic framework for drug compatibility and has guided the formation of a large number of classic formulas.However,due to the complex compositions and diverse mechanisms of action of TCM,it is difficult to comprehensively reveal its potential synergistic patterns using traditional methods.Synergistic prediction based on molecular compatibility theory provides new ideas for identifying combinations of active compounds in TCM.Compared to resource-intensive traditional experimental methods,artificial intelligence possesses the ability to mine synergistic patterns from multi-omics and structural data,providing an efficient means for modeling and optimizing TCM combinations.This paper systematically reviews the application progress of AI in the synergistic prediction of TCM active compounds and explores the challenges and prospects of its application in modeling combination relationships,thereby contributing to the modernization of TCM theory and methodological innovation. 展开更多
关键词 Molecular compatibility theory Synergy prediction of tcm compounds Molecular drugs combination prediction Artificial intelligence
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