Explainable Artificial Intelligence in the Field of Drug Research

作者：Qingyao Ding, Rufan Yao, Yue Bai, Limu Da, Yujiang Wang, Rongwu Xiang, Xiwei Jiang, Fei Zhai · 发表于：Drug Design Development and Therapy · 年份：2025 · DOI：10.2147/dddt.s525171 · 被引用次数：29 · 研究领域：Computational Drug Discovery Methods、Cell Image Analysis Techniques、Explainable Artificial Intelligence (XAI)

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.