药物发现
转化式学习
药物开发
计算机科学
药品
障碍物
工程伦理学
风险分析(工程)
数据科学
管理科学
医学
工程类
药理学
生物信息学
心理学
政治学
生物
教育学
法学
作者
Yilun Zhang,Mohamed Mastouri,Yang Zhang
出处
期刊:Med
[Elsevier]
日期:2024-08-23
卷期号:5 (9): 1050-1070
标识
DOI:10.1016/j.medj.2024.07.026
摘要
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.
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