CHINESE JOURNAL OF MEDICINAL GUIDE >
Applications, Challenges and
Regulatory Strategies of Generative Artificial Intelligence in Drug Development
Received date: 2025-06-25
Revised date: 2025-11-25
Accepted date: 2026-02-06
Online published: 2026-02-11
Generative artificial intelligence (Gen AI) is reshaping various stages of the pharmaceutical development process, including target discovery, molecular design, clinical trial optimization, and regulatory document generation. By utilizing deep learning models to analyze multi-source data, Gen AI enhances drug target identification, accelerates the design and screening of candidate molecules, and optimizes clinical trial designs and patient recruitment. Although Gen AI shows tremendous potential in enhancing the efficiency and success rates of R&D, its application still faces challenges such as insufficient interpretability, data privacy protection and compliance issues, model bias, a shortage of specialized talent and interdisciplinary knowledge gaps, lagging global regulatory standards and guidelines, and difficulties in replicating successful cases.To address these issues, drug regulatory agencies are exploring pilot initiatives to advance regulatory science practices, promoting collaborative governance to tackle technical challenges, and seizing development opportunities to lead the high-quality growth of the pharmaceutical industry, thereby promoting regulatory support and compliance management for AI technologies. Through policy guidance and industrial collaboration, Gen AI is poised to further drive innovation in drug development and contribute to the high-quality advancement of the biopharmaceutical industry.
Hai-Ling LI
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Applications, Challenges and
Regulatory Strategies of Generative Artificial Intelligence in Drug Development
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