View in EDS

Explainable Artificial Intelligence in the Field of Drug Research

Saved in:
Bibliographic Details
Title: Explainable Artificial Intelligence in the Field of Drug Research
Authors: Ding Q, Yao R, Bai Y, Da L, Wang Y, Xiang R, Jiang X, Zhai F
Source: Drug Des Devel Ther
Drug Design, Development and Therapy, Vol Volume 19, Iss Issue 1, Pp 4501-4516 (2025)
Publisher Information: Informa UK Limited, 2025.
Publication Year: 2025
Subject Terms: bibliometric analysis, Explainable Artificial Intelligence, XAI, Drug Research, Therapeutics. Pharmacology, RM1-950, Review, shapley additive explanations, interpretability
Description: 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.
Document Type: Article
Other literature type
Language: English
ISSN: 1177-8881
DOI: 10.2147/dddt.s525171
Access URL: https://doaj.org/article/db2f40b78bc94a10b796261cbaae9086
Rights: CC BY NC
URL: http://creativecommons.org/licenses/by-nc/4.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at http://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v4.0) License (http://creativecommons.org/licenses/by-nc/4.0/ (http://creativecommons.org/licenses/by-nc/4.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (http://www.dovepress.com/terms.php).
Accession Number: edsair.doi.dedup.....f10aba6cda238f4d2dbfa2bcc962da26
Database: OpenAIRE
Description
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.
ISSN:11778881
DOI:10.2147/dddt.s525171