Explainable Artificial Intelligence in the Field of Drug Research.

IF 4.7 2区医学 Q1 CHEMISTRY, MEDICINAL

Drug Design, Development and Therapy Pub Date : 2025-05-29 eCollection Date: 2025-01-01 DOI:10.2147/DDDT.S525171

Qingyao Ding, Rufan Yao, Yue Bai, Limu Da, Yujiang Wang, Rongwu Xiang, Xiwei Jiang, Fei Zhai

{"title":"Explainable Artificial Intelligence in the Field of Drug Research.","authors":"Qingyao Ding, Rufan Yao, Yue Bai, Limu Da, Yujiang Wang, Rongwu Xiang, Xiwei Jiang, Fei Zhai","doi":"10.2147/DDDT.S525171","DOIUrl":null,"url":null,"abstract":"<p><p>In recent years, the widespread use of artificial intelligence (AI) and big data technologies in drug research has significantly accelerated the drug development process. However, their black-box nature makes it challenging to evaluate their effectiveness and safety. The interpretability of models has become a key issue in the application of AI in the drug development. In this paper, a bibliometric approach has been adopted to systematically analyze the application of Explainable Artificial Intelligence (XAI) techniques in drug research, with an in-depth discussion of the developmental trends, geographical distribution, journal preferences, major contributors, and research hotspots. In addition, the research results of XAI are summarized in the three directions of chemical, biological, and traditional Chinese medicine, and the future research directions and development trends are envisioned in order to promote the in-depth application of XAI technology in drug discovery and continuous innovation.</p>","PeriodicalId":11290,"journal":{"name":"Drug Design, Development and Therapy","volume":"19 ","pages":"4501-4516"},"PeriodicalIF":4.7000,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12129466/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Drug Design, Development and Therapy","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2147/DDDT.S525171","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"CHEMISTRY, MEDICINAL","Score":null,"Total":0}

引用次数: 0

Abstract

In recent years, the widespread use of artificial intelligence (AI) and big data technologies in drug research has significantly accelerated the drug development process. However, their black-box nature makes it challenging to evaluate their effectiveness and safety. The interpretability of models has become a key issue in the application of AI in the drug development. In this paper, a bibliometric approach has been adopted to systematically analyze the application of Explainable Artificial Intelligence (XAI) techniques in drug research, with an in-depth discussion of the developmental trends, geographical distribution, journal preferences, major contributors, and research hotspots. In addition, the research results of XAI are summarized in the three directions of chemical, biological, and traditional Chinese medicine, and the future research directions and development trends are envisioned in order to promote the in-depth application of XAI technology in drug discovery and continuous innovation.

查看原文本刊更多论文

药物研究领域中可解释的人工智能。

近年来，人工智能（AI）和大数据技术在药物研究中的广泛应用，大大加快了药物开发进程。然而，它们的黑箱性质使得评估它们的有效性和安全性具有挑战性。模型的可解释性已成为人工智能在药物开发中应用的关键问题。本文采用文献计量学方法，系统分析了可解释人工智能（Explainable Artificial Intelligence， XAI）技术在药物研究中的应用，并对其发展趋势、地理分布、期刊偏好、主要贡献者和研究热点进行了深入探讨。此外，从化学、生物、中药三个方向总结了XAI的研究成果，展望了未来的研究方向和发展趋势，以促进XAI技术在药物发现和不断创新中的深入应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Drug Design, Development and Therapy CHEMISTRY, MEDICINAL-PHARMACOLOGY & PHARMACY

CiteScore

9.00

自引率

0.00%

发文量

382

审稿时长

>12 weeks

期刊介绍： Drug Design, Development and Therapy is an international, peer-reviewed, open access journal that spans the spectrum of drug design, discovery and development through to clinical applications. The journal is characterized by the rapid reporting of high-quality original research, reviews, expert opinions, commentary and clinical studies in all therapeutic areas. Specific topics covered by the journal include: Drug target identification and validation Phenotypic screening and target deconvolution Biochemical analyses of drug targets and their pathways New methods or relevant applications in molecular/drug design and computer-aided drug discovery* Design, synthesis, and biological evaluation of novel biologically active compounds (including diagnostics or chemical probes) Structural or molecular biological studies elucidating molecular recognition processes Fragment-based drug discovery Pharmaceutical/red biotechnology Isolation, structural characterization, (bio)synthesis, bioengineering and pharmacological evaluation of natural products** Distribution, pharmacokinetics and metabolic transformations of drugs or biologically active compounds in drug development Drug delivery and formulation (design and characterization of dosage forms, release mechanisms and in vivo testing) Preclinical development studies Translational animal models Mechanisms of action and signalling pathways Toxicology Gene therapy, cell therapy and immunotherapy Personalized medicine and pharmacogenomics Clinical drug evaluation Patient safety and sustained use of medicines.