Autonomous chemical research with large language models

被引:343
作者
Boiko, Daniil A. [1 ]
Macknight, Robert [1 ]
Kline, Ben [2 ]
Gomes, Gabe [1 ,3 ,4 ]
机构
[1] Carnegie Mellon Univ, Dept Chem Engn, Pittsburgh, PA 15213 USA
[2] Emerald Cloud Lab, South San Francisco, CA USA
[3] Carnegie Mellon Univ, Dept Chem, Pittsburgh, PA 15213 USA
[4] Carnegie Mellon Univ, Wilton E Scott Inst Energy Innovat, Pittsburgh, PA 15213 USA
基金
美国国家科学基金会; 美国安德鲁·梅隆基金会;
关键词
RECEPTOR; 1; TRACE AMINES; TAAR1; METHAMPHETAMINE; PHARMACOLOGY; AMPHETAMINE; MECHANISMS; DISCOVERY; INSIGHTS; REVEAL;
D O I
10.1038/s41586-023-06792-0
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Transformer-based large language models are making significant strides in various fields, such as natural language processing1-5, biology6,7, chemistry8-10 and computer programming11,12. Here, we show the development and capabilities of Coscientist, an artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation. Coscientist showcases its potential for accelerating research across six diverse tasks, including the successful reaction optimization of palladium-catalysed cross-couplings, while exhibiting advanced capabilities for (semi-)autonomous experimental design and execution. Our findings demonstrate the versatility, efficacy and explainability of artificial intelligence systems like Coscientist in advancing research. Coscientist is an artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation.
引用
收藏
页码:570 / +
页数:13
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