Article(id=1321426300183629849, tenantId=1146029695717560320, journalId=1235980733773295621, issueId=1321426285826519296, articleNumber=null, orderNo=null, doi=10.20251/j.cnki.1003-3734.2026.16.008, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=null, revisedDateStr=null, acceptedDate=1764604800000, acceptedDateStr=2025-12-02, onlineDate=1792982180877, onlineDateStr=2026-10-26, pubDate=1788019200000, pubDateStr=2026-08-30, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792982180877, onlineIssueDateStr=2026-10-26, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792982180877, creator=13701087609, updateTime=1792982180877, updator=13701087609, issue=Issue{id=1321426285826519296, tenantId=1146029695717560320, journalId=1235980733773295621, year='2026', volume='35', issue='16', pageStart='1681', pageEnd='1792', issueExtLink='null', onlineDate='null', pubDate='1788019200000', pubDateStr='2026-08-30', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792982177454, creator='13701087609', updateTime=1792982881356, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1321429238297776921, tenantId=1146029695717560320, journalId=1235980733773295621, issueId=1321426285826519296, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1321429238297776922, tenantId=1146029695717560320, journalId=1235980733773295621, issueId=1321426285826519296, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1736, endPage=1744, ext={EN=ArticleExt(id=1321426300443676698, articleId=1321426300183629849, tenantId=1146029695717560320, journalId=1235980733773295621, language=EN, title=Advances in the application of artificial intelligence and big data in the field of anti-infective drugs, columnId=null, journalTitle=Chinese Journal of New Drugs, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Infectious diseases pose a serious threat to global public health, making the research, development, and quality control of anti-infective drugs of critical importance. In recent years, artificial intelligence (AI) and big data have accelerated the drug discovery process and improved drug quality control levels by leveraging their efficient data processing capabilities and pattern recognition abilities. This article reviews the application of AI and big data in anti-infective drug molecular screening, resistance mechanism research, and broad-spectrum candidate drug development, while also summarizing their key roles in pharmaceutical production and quality control processes. However, the application of AI and big data technology in the development and quality control of anti-infective drugs still has limitations. In the future, it is necessary to promote the integration of interdisciplinary approaches and improve algorithm accuracy to drive the development and quality control of anti-infective drugs toward a new phase of efficiency and precision.

, authors=Xin-yu GUAN^{1, 2}, Qian LIU^{1, 2}, Jin LI², Yi-wen ZHANG¹, Yu ZHAO^{2, *}, authorsList=Xin-yu GUAN, Qian LIU, Jin LI, Yi-wen ZHANG, Yu ZHAO, authorCompany=null, correspAuthors=Yu ZHAO, 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=1321426300741472283, articleId=1321426300183629849, tenantId=1146029695717560320, journalId=1235980733773295621, language=CN, title=人工智能与大数据在抗感染药物领域中的应用进展, columnId=1236330553964810454, journalTitle=中国新药杂志, columnName=综述, runingTitle=null, highlight=null, articleAbstract=

感染性疾病严重威胁全球公共健康，抗感染药物的研发与质量控制至关重要。近年来，人工智能（artificial intelligence, AI）与大数据凭借高效的数据处理能力和模式识别能力，加速药物发现过程，提高药物质量控制水平。本文综述了AI与大数据技术在抗感染药物分子筛选、耐药机制研究及广谱候选药物开发中的应用进展，同时总结了其在药品生产与质量控制过程中的关键作用。尽管如此，AI与大数据技术在抗感染药物研发和质量控制上的应用仍存在不足，未来须通过跨学科融合及提高算法准确性等方法，推动抗感染药物研发和质量控制迈向高效、精准的新阶段。

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关欣宇，女，硕士研究生，研究方向：药品质量研究。E-mail: 13214966909@163.com。

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关欣宇，女，硕士研究生，研究方向：药品质量研究。E-mail: 13214966909@163.com。

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