Artificial intelligence-driven paradigm transformation in biopharmaceutical R&D: Applications and emerging scenarios

作者：Lili LIU, Mingyue ZHENG, Ye YUAN, Xutong LI, Rong FAN, Wei Wei, Jinxin ZHAO, Guobin QI, Hua YUE, Likun Gong, Songping Zhang, Jiachen LI, Yuchen SUN, Xiaoyan CHEN, Yao CHEN, Xin LIU, Xiao ZHANG, Yuehong GAO, Jianfeng LI, Kaixian CHEN, 马光辉, Jianmin YUE · 发表于：Zhongguo Kexueyuan yuankan · 年份：2026 · DOI：10.3724/j.issn.1000-3045.20260420005 · 研究领域：Computational Drug Discovery Methods、Statistical Methods in Clinical Trials、3D Printing in Biomedical Research

The biopharmaceutical industry is a critical domain underpinning national scientific and technological innovation development and public health. With the rapid advancement of artificial intelligence (AI) and its deep integration with the life sciences, biomedicine research is undergoing a paradigm shift from traditional experience-driven trial-and-error approaches to data-driven and predictive validation-based models. This study systematically examines the pathways for reshaping research in biomedicine paradigms under the convergence of data-driven, mechanism-driven, and intelligence-driven approaches. It focuses on recent advances in the application of AI across key stages, including drug discovery and design, druggability evaluation, delivery system design and optimization, nonclinical and clinical research, and intelligent manufacturing. Building on these analyses, the study further explores future development trends, highlighting that the integration of multimodal intelligence with mechanistic interpretability, as well as the construction of data-centric collaborative innovation ecosystems, will serve as key drivers for the high-quality development of the AI-enabled biopharmaceutical industry.