回顾性分析
有机溶剂
干预(咨询)
计算机科学
机器人学
流量(数学)
序列(生物学)
人工智能
有机合成
化学
机器学习
机器人
工程类
数学
化学工程
有机化学
催化作用
精神科
生物化学
全合成
几何学
心理学
作者
Connor W. Coley,Dale A. Thomas,Justin A. M. Lummiss,Jonathan N. Jaworski,C. Breen,Victor Schultz,Travis Hart,Joshua Fishman,Luke Rogers,Hanyu Gao,Robert W. Hicklin,Pieter Plehiers,Joshua Byington,John S. Piotti,William H. Green,A. John Hart,Timothy F. Jamison,Klavs F. Jensen
出处
期刊:Science
[American Association for the Advancement of Science]
日期:2019-08-08
卷期号:365 (6453)
被引量:1068
标识
DOI:10.1126/science.aax1566
摘要
The synthesis of complex organic molecules requires several stages, from ideation to execution, that require time and effort investment from expert chemists. Here, we report a step toward a paradigm of chemical synthesis that relieves chemists from routine tasks, combining artificial intelligence-driven synthesis planning and a robotically controlled experimental platform. Synthetic routes are proposed through generalization of millions of published chemical reactions and validated in silico to maximize their likelihood of success. Additional implementation details are determined by expert chemists and recorded in reusable recipe files, which are executed by a modular continuous-flow platform that is automatically reconfigured by a robotic arm to set up the required unit operations and carry out the reaction. This strategy for computer-augmented chemical synthesis is demonstrated for 15 drug or drug-like substances.
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