药物发现
时间轴
计算机科学
药物开发
生成语法
药品
数据科学
风险分析(工程)
药物重新定位
人工智能
医学
生物信息学
药理学
生物
考古
历史
作者
Amit Gangwal,Antonio Lavecchia
标识
DOI:10.1016/j.drudis.2024.103992
摘要
Artificial intelligence (AI) is revolutionizing drug discovery by enhancing precision, reducing timelines and costs, and enabling AI-driven computer-aided drug design. This review focuses on recent advancements in deep generative models (DGMs) for de novo drug design, exploring diverse algorithms and their profound impact. It critically analyses the challenges that are intricately interwoven into these technologies, proposing strategies to unlock their full potential. It features case studies of both successes and failures in advancing drugs to clinical trials with AI assistance. Last, it outlines a forward-looking plan for optimizing DGMs in de novo drug design, thereby fostering faster and more cost-effective drug development.
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