Zhang Kang, Yang Xin, Wang Yifei, Yu Yunfang, Huang Niu, Li Gen, Li Xiaokun, Wu Joseph C, Yang Shengyong
Eye Hospital and Institute for Advanced Study on Eye Health and Diseases, Institute for clinical Data Science, Wenzhou Medical University, Wenzhou, China.
State Key Laboratory of Macromolecular Drugs and Large-Scale Preparation, Wenzhou Medical University, Wenzhou, China.
Nat Med. 2025 Jan;31(1):45-59. doi: 10.1038/s41591-024-03434-4. Epub 2025 Jan 20.
Drug development is a complex and time-consuming endeavor that traditionally relies on the experience of drug developers and trial-and-error experimentation. The advent of artificial intelligence (AI) technologies, particularly emerging large language models and generative AI, is poised to redefine this paradigm. The integration of AI-driven methodologies into the drug development pipeline has already heralded subtle yet meaningful enhancements in both the efficiency and effectiveness of this process. Here we present an overview of recent advancements in AI applications across the entire drug development workflow, encompassing the identification of disease targets, drug discovery, preclinical and clinical studies, and post-market surveillance. Lastly, we critically examine the prevailing challenges to highlight promising future research directions in AI-augmented drug development.
药物研发是一项复杂且耗时的工作,传统上依赖于药物研发人员的经验以及反复试验。人工智能(AI)技术的出现,尤其是新兴的大语言模型和生成式AI,有望重新定义这一模式。将人工智能驱动的方法整合到药物研发流程中,已经在这一过程的效率和有效性方面带来了细微但有意义的提升。在此,我们概述了人工智能在整个药物研发工作流程中的最新进展,包括疾病靶点的识别、药物发现、临床前和临床研究以及上市后监测。最后,我们审慎审视了当前面临的挑战,以突出人工智能辅助药物研发中未来有前景的研究方向。