Article(id=1304414701593584077, tenantId=1146029695717560320, journalId=1302319053441957962, issueId=1304414700964443026, articleNumber=null, orderNo=null, doi=10.7501/j.issn.0253-2670.2026.04.001, pmid=null, cstr=null, oa=null, hot=0, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1760889600000, receivedDateStr=2025-10-20, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1788926299712, onlineDateStr=2026-09-09, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1788926299712, onlineIssueDateStr=2026-09-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1788926299712, creator=13701087609, updateTime=1788926299712, updator=13701087609, issue=Issue{id=1304414700964443026, tenantId=1146029695717560320, journalId=1302319053441957962, year='2026', volume='57', issue='4', pageStart='1209', pageEnd='1596', issueExtLink='null', onlineDate='null', pubDate='1772208000000', pubDateStr='2026-02-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1788926299563, creator='13701087609', updateTime=1788926573099, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1304415848316297970, tenantId=1146029695717560320, journalId=1302319053441957962, issueId=1304414700964443026, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1304415848316297971, tenantId=1146029695717560320, journalId=1302319053441957962, issueId=1304414700964443026, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1209, endPage=1220, ext={EN=ArticleExt(id=1304414703510381007, articleId=1304414701593584077, tenantId=1146029695717560320, journalId=1302319053441957962, language=EN, title=Progress and application prospects of artificial intelligence promoting reshaping of research and development paradigms for classic famous prescriptions, columnId=null, journalTitle=Chinese Traditional and Herbal Drugs, columnName=null, runingTitle=null, highlight=null, articleAbstract=As the core carrier of the theoretical system of traditional Chinese medicine (TCM), classic famous prescriptions play a vital role in the prevention and treatment of chronic diseases and major diseases. However, their secondary development still faces numerous technical bottlenecks, such as limitations caused by insufficient data standardization and inadequate evidence chains in evidence-based medicine, which restrict the transformation process from clinical practice to industrialization. Artificial intelligence (AI) has promoted the shift of traditional empirical medicine to a new research paradigm of “algorithm-model-data-scenario-application”, providing a brand-new perspective for data mining, prescription optimization, and new drug research and development of classic famous prescriptions, and empowering the modernization of TCM. This article systematically investigates how AI is reshaping the research paradigm of classic famous prescriptions, proposing an integrated framework centered on intelligent data mining, in-depth mechanism analysis, and precise efficacy evaluation. Firstly, it elaborates on the foundational basis of key technologies such as machine learning and their applicable scenarios. Secondly, from application dimensions including intelligent screening of candidate prescriptions, analysis of the material basis for efficacy, and in-depth exploration of mechanisms of action, it summarizes the research progress and application prospects of the integration of AI and classic famous prescriptions. Finally, it analyzes challenges such as data heterogeneity, lack of standards, and poor adaptability between models and TCM theories, and proposes targeted solutions, aiming to provide references for AI empowering the secondary development of classic famous prescriptions., authors=HAN Xingxing, ZHU Huaxu, TANG Zhishu, ZHAO Ranran, LIU Yanru, ZHU Baojie, FU Tingming, ZHANG Yue, XIAO Qingqing, LI Bo, LIU Hongbo, authorsList=HAN Xingxing, ZHU Huaxu, TANG Zhishu, ZHAO Ranran, LIU Yanru, ZHU Baojie, FU Tingming, ZHANG Yue, XIAO Qingqing, LI Bo, LIU Hongbo, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1304414701857825230, articleId=1304414701593584077, tenantId=1146029695717560320, journalId=1302319053441957962, language=CN, title=人工智能推动经典名方研发范式重塑的进展与应用展望, columnId=1304140187961946198, journalTitle=中草药, columnName=专论, runingTitle=null, highlight=null, articleAbstract=经典名方作为中医药理论体系的核心载体，在慢性疾病及重大疾病的防治中具有重要作用。然而，其二次开发面临着诸多挑战，如数据标准化、循证医学证据链不足等，制约了其从临床到产业转化的步伐。人工智能推动传统经验医学转向“算法-模型-数据-场景-应用”的研究新范式，为经典名方的数据挖掘、处方优化与新药研发提供了崭新的视野，赋能中医药现代化发展。系统探讨人工智能推动经典名方研究范式重塑的技术路径，提出“智能挖掘-机制解析-精准评价”三位一体的研究范式。首先，阐述机器学习等关键技术基础及其适配场景；其次，从候选方剂智能筛选、药效物质基础解析及作用机制深度挖掘等应用维度，总结人工智能与经典名方融合的研究进展及应用前景；最后，剖析数据异构化、标准缺失及模型与中医药理论适配性差等挑战，并提出针对性解决策略，为构建人工智能赋能经典名方二次开发的研究新范式提供参考。, authors=韩星星¹, 朱华旭¹, 唐志书², 赵冉冉³, 刘妍如⁴, 朱宝杰¹, 付廷明¹, 张悦¹, 肖青青¹, 李博¹, 刘红波⁴, authorsList=韩星星, 朱华旭, 唐志书, 赵冉冉, 刘妍如, 朱宝杰, 付廷明, 张悦, 肖青青, 李博, 刘红波, authorCompany=1 南京中医药大学, 江苏省植物药深加工工程研究中心, 江苏省中药资源产业化过程协同创新中心, 江苏南京 210023;
2 北京中医药大学, 北京 102488;
3 中国中医科学院研究生院, 北京 100700;
4 陕西中医药大学, 陕西中药资源产业化部省共建协同创新中心, 陕西 咸阳 712046, correspAuthors=朱华旭, authorNote=韩星星: 韩星星，博士，研究方向为经典名方的智能解析。E-mail:hxx0307@163.com, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=vsI9U7zIB/TCH7KGRuJ5sQ==, pdfFileSize=1138392, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=国家自然科学基金面上项目 (82274222); 国家自然科学基金面上项目 (82274107); 2021年岐黄学者支持项目 (国中医药人教函[2022]6号))}, authors=null, keywords=[Keyword(id=1304414703661375952, tenantId=1146029695717560320, journalId=1302319053441957962, articleId=1304414701593584077, language=CN, orderNo=1, keyword=人工智能), Keyword(id=1304414703745262033, tenantId=1146029695717560320, journalId=1302319053441957962, articleId=1304414701593584077, language=CN, orderNo=2, keyword=经典名方), 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