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REVIEW ARTICLE

Application and design of artificial intelligence in computer-aided drug design for ischemic stroke

Shutong Pang1 ,  Yuehui Liao1 ,  Yu Hu1 ,  Yu Chen1 ,  Xiaobo Lai1* ,  Panfei Li1*
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1 School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China
AIH, 026170038 https://doi.org/10.36922/AIH026170038
Received: 23 April 2026 | Revised: 21 May 2026 | Accepted: 27 May 2026 | Published online: 16 June 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

The development of drugs for ischemic stroke has long been plagued by the dilemma of high investment, high attrition, and low translational success rates. This narrative review summarizes the application of artificial intelligence (AI) in ischemic stroke drug design, specifically encompassing intelligent target mining, the generation and optimization of lead compounds, early prediction of pharmacokinetics/toxicity (ADMET), and the optimization of clinical trial protocols. Although AI-aided drug design has progressed from a theoretical concept to practical validation, significant challenges remain in data quality, model interpretability, and clinical translation. However, with ongoing efforts such as the construction of high-quality multimodal databases and the advancement of explainable AI models, artificial intelligence holds considerable promise for continuously driving the discovery of novel therapeutics for stroke treatment.

Graphical abstract
Keywords
Artificial intelligence
Ischemic stroke
Computer-aided drug design
Drug discovery
Machine learning
Funding
This work was supported by the National Natural Science Foundation of China (No. 82505804); the Hangzhou Agricultural and Social Development Key Project (No. 20231203A12); and the “Pioneer” and “Leading Goose” + “X” R&D Program of Zhejiang (No. 2025C02201).
Conflict of interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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