@article{bibcite_7801,
  keywords = {Artificial intelligence, Drug development, Drug Discovery, Humans},
  author = {Kang Zhang and Xin Yang and Yifei Wang and Yunfang Yu and Niu Huang and Gen Li and Xiaokun Li and Joseph C. Wu and Shengyong Yang},
  title = {Artificial intelligence in drug development},
  abstract = {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.},
  year = {2025},
  journal = {Nature Medicine},
  volume = {31},
  pages = {45-59},
  month = {2025-01},
  issn = {1546-170X},
  doi = {10.1038/s41591-024-03434-4},
  language = {eng},
}