College of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, China; School of Medicine, The Chinese University of Hong Kong (Shenzhen), Shenzhen, Guangdong, China.
College of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, China.
Med. 2024 Sep 13;5(9):1050-1070. doi: 10.1016/j.medj.2024.07.026. Epub 2024 Aug 21.
Artificial intelligence (AI) has profoundly advanced the field of biomedical research, which also demonstrates transformative capacity for innovation in drug development. This paper aims to deliver a comprehensive analysis of the progress in AI-assisted drug development, particularly focusing on small molecules, RNA, and antibodies. Moreover, this paper elucidates the current integration of AI methodologies within the industrial drug development framework. This encompasses a detailed examination of the industry-standard drug development process, supplemented by a review of medications presently undergoing clinical trials. Conclusively, the paper tackles a predominant obstacle within the AI pharmaceutical sector: the absence of AI-conceived drugs receiving approval. This paper also advocates for the adoption of large language models and diffusion models as a viable strategy to surmount this challenge. This review not only underscores the significant potential of AI in drug discovery but also deliberates on the challenges and prospects within this dynamically progressing field.
人工智能（AI）在生物医学研究领域取得了深远的进展，这也证明了其在药物开发方面具有创新的变革能力。本文旨在对人工智能辅助药物开发的进展进行全面分析，特别关注小分子、RNA 和抗体。此外，本文还阐述了当前人工智能方法在工业药物开发框架中的整合。这包括对行业标准药物开发流程的详细检查，并辅以对正在进行临床试验的药物的审查。最后，本文解决了人工智能制药领域的一个主要障碍：没有获得批准的人工智能设计的药物。本文还提倡采用大型语言模型和扩散模型作为克服这一挑战的可行策略。本综述不仅强调了人工智能在药物发现中的巨大潜力，还讨论了这一快速发展领域中的挑战和前景。
