Network pharmacology: Advancing the application of large language models in traditional Chinese medicine research
Abstract. Traditional Chinese medicine (TCM) is characterized by complex, multicomponent herbal formulations that challenge the conventional “one drug, one target” paradigm. Network pharmacology, through the construction of multilayered drug-target-disease networks, provides a systematic framework f...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Health/LWW
2025-06-01
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| Series: | Science of Traditional Chinese Medicine |
| Online Access: | http://journals.lww.com/10.1097/st9.0000000000000073 |
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| _version_ | 1849429504205783040 |
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| author | Qingyuan Liu Dingfan Zhang Boyang Wang Weibo Zhao Tingyu Zhang Chayanis Sutcharitchan Shao Li |
| author_facet | Qingyuan Liu Dingfan Zhang Boyang Wang Weibo Zhao Tingyu Zhang Chayanis Sutcharitchan Shao Li |
| author_sort | Qingyuan Liu |
| collection | DOAJ |
| description | Abstract. Traditional Chinese medicine (TCM) is characterized by complex, multicomponent herbal formulations that challenge the conventional “one drug, one target” paradigm. Network pharmacology, through the construction of multilayered drug-target-disease networks, provides a systematic framework for unraveling TCM’s multitarget and multipathway mechanisms. Recent advancements in artificial intelligence, particularly large language models (LLMs), further enhance data integration, target identification, and clinical decision-making. This review synthesizes current progress in the application of network pharmacology and LLMs in TCM, highlighting their potential to deepen mechanistic insights and optimize drug discovery. By bridging traditional medical wisdom with modern computational tools, this integrative approach aims to advance the scientific validation of TCM and foster innovative healthcare solutions. |
| format | Article |
| id | doaj-art-44ebf6d697ab48aa8598e1dc2b54bf5b |
| institution | Kabale University |
| issn | 2836-922X 2836-9211 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wolters Kluwer Health/LWW |
| record_format | Article |
| series | Science of Traditional Chinese Medicine |
| spelling | doaj-art-44ebf6d697ab48aa8598e1dc2b54bf5b2025-08-20T03:28:21ZengWolters Kluwer Health/LWWScience of Traditional Chinese Medicine2836-922X2836-92112025-06-013211312310.1097/st9.0000000000000073202506000-00002Network pharmacology: Advancing the application of large language models in traditional Chinese medicine researchQingyuan Liu0Dingfan Zhang1Boyang Wang2Weibo Zhao3Tingyu Zhang4Chayanis Sutcharitchan5Shao Li6Institute of TCM-X, Department of Automation, Tsinghua University, Beijing, ChinaInstitute of TCM-X, Department of Automation, Tsinghua University, Beijing, ChinaInstitute of TCM-X, Department of Automation, Tsinghua University, Beijing, ChinaInstitute of TCM-X, Department of Automation, Tsinghua University, Beijing, ChinaInstitute of TCM-X, Department of Automation, Tsinghua University, Beijing, ChinaInstitute of TCM-X, Department of Automation, Tsinghua University, Beijing, ChinaInstitute of TCM-X, Department of Automation, Tsinghua University, Beijing, ChinaAbstract. Traditional Chinese medicine (TCM) is characterized by complex, multicomponent herbal formulations that challenge the conventional “one drug, one target” paradigm. Network pharmacology, through the construction of multilayered drug-target-disease networks, provides a systematic framework for unraveling TCM’s multitarget and multipathway mechanisms. Recent advancements in artificial intelligence, particularly large language models (LLMs), further enhance data integration, target identification, and clinical decision-making. This review synthesizes current progress in the application of network pharmacology and LLMs in TCM, highlighting their potential to deepen mechanistic insights and optimize drug discovery. By bridging traditional medical wisdom with modern computational tools, this integrative approach aims to advance the scientific validation of TCM and foster innovative healthcare solutions.http://journals.lww.com/10.1097/st9.0000000000000073 |
| spellingShingle | Qingyuan Liu Dingfan Zhang Boyang Wang Weibo Zhao Tingyu Zhang Chayanis Sutcharitchan Shao Li Network pharmacology: Advancing the application of large language models in traditional Chinese medicine research Science of Traditional Chinese Medicine |
| title | Network pharmacology: Advancing the application of large language models in traditional Chinese medicine research |
| title_full | Network pharmacology: Advancing the application of large language models in traditional Chinese medicine research |
| title_fullStr | Network pharmacology: Advancing the application of large language models in traditional Chinese medicine research |
| title_full_unstemmed | Network pharmacology: Advancing the application of large language models in traditional Chinese medicine research |
| title_short | Network pharmacology: Advancing the application of large language models in traditional Chinese medicine research |
| title_sort | network pharmacology advancing the application of large language models in traditional chinese medicine research |
| url | http://journals.lww.com/10.1097/st9.0000000000000073 |
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