Drug research and development (R&D) is characterized by long cycles, high costs, and low clinical success rates, and traditional R&D paradigms have struggled to meet the demands of complex diseases and innovative drug development. In recent years, artificial intelligence (AI), leveraging its strengths in multi−source data fusion, complex pattern recognition, and intelligent decision−making, has been driving a paradigm shift in drug R&D from experience−driven to data−driven and knowledge−driven approaches. This article systematically reviews the latest advances in AI−empowered drug R&D worldwide, focusing on key applications across the full drug R&D pipeline and technological development trends, and synthesizes representative studies to summarize AI's vital roles in improving R&D efficiency, optimizing decision−making, and fostering paradigm changes. The review shows that AI has become an essential bridge linking biomedical data, computational models, and experimental validation. It has not only significantly enhanced the efficiency of early−stage target discovery, molecular design, and drug evaluation, but has also spurred the development of novel R&D modalities such as automated experimentation, self−driving laboratories, and virtual cells, thereby providing new technical pathways toward an intelligent drug R&D system. However, the broad application of AI in drug R&D remains constrained by insufficient high−quality data, limited model interpretability and generalizability, difficulties in multimodal knowledge fusion, and the lack of robust experimental validation and standardized evaluation frameworks; its clinical translational value thus requires further verification. Looking ahead, efforts should be directed toward strengthening high−quality data resources and sharing systems, developing interpretable AI models that integrate biological mechanisms and physical laws, promoting deep integration of generative AI, multi−agent systems, and self−driving laboratories, refining model evaluation standards and regulatory frameworks, and fostering collaborative innovation between AI and experimental sciences, so as to accelerate the evolution of AI−empowered drug R&D toward intelligent, automated, and closed−loop paradigms.

artificial intelligence  /  drug R&D  /  intelligent drug design  /  generative artificial intelligence  /  self−driving laboratories  /  virtual cells