Optimizing chemical reaction conditions using deep learning: a case study for the Suzuki-Miyaura cross-coupling reaction

doi:10.1039/d0qo00544d

	Optimizing chemical reaction conditions using deep learning: a case study for the Suzuki-Miyaura cross-coupling reaction
	Fu, Zunyun 1,2; Li, Xutong 2,3; Wang, Zhaohui 4; Li, Zhaojun 2; Liu, Xiaohong 2; Wu, Xiaolong 2; Zhao, Jihui 2,3; Ding, Xiaoyu 2,3; Wan, Xiaozhe 2,3; Zhong, Feisheng 2,3
刊名	ORGANIC CHEMISTRY FRONTIERS
	2020-08-21
卷号	7 期号:16 页码:2269-2277
ISSN号	2052-4129
DOI	10.1039/d0qo00544d
通讯作者	Wang, Jiang(jwang@simm.ac.cn) ; Jiang, Hualiang(hljiang@simm.ac.cn) ; Zheng, Mingyue(myzheng@simm.ac.cn)
英文摘要	Here we report a feasibility study of a deep learning model for exploring the optimal reaction conditions for given chemical reactions. The model was trained to learn the relationships between the chemical contexts, reaction conditions and product yields based on high-quality existing experimental data, and then extrapolate reasonably to unseen reactions byin silicoexploration of accessible reaction space. This strategy was applied to the Suzuki-Miyaura cross-coupling reaction to find the best catalysts for given reactants and at the same time to discover the optimum combination of the reaction conditions. We demonstrated that the trained model was able to determine the productive catalysts as well as the most favorable catalyst loading and reaction temperature for both modeled reactions and external unseen reactions. This work aims to provide an insight into the feasibility of introducing a deep learning method in the optimization of chemical reaction conditions.
资助项目	National Natural Science Foundation of China[21632008] ; National Natural Science Foundation of China[81620108027] ; National Natural Science Foundation of China[81773634] ; State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, China[KF-GN-201706] ; National Science & Technology Major Project Key New Drug Creation and Manufacturing Program, China[2018ZX09711002] ; Personalized MedicinesMolecular Signature-based Drug Discovery and Development, Strategic Priority Research Program of the Chinese Academy of Sciences[XDA12050201]
WOS关键词	CATALYST ; SELECTION ; OPTIMIZATION
WOS研究方向	Chemistry
语种	英语
出版者	ROYAL SOC CHEMISTRY
WOS记录号	WOS:000558942300013
内容类型	期刊论文
源URL	[http://119.78.100.183/handle/2S10ELR8/292300]
专题	中国科学院上海药物研究所
通讯作者	Wang, Jiang; Jiang, Hualiang; Zheng, Mingyue
作者单位	1.Nanjing Univ Chinese Med, Sch Chinese Mat Med, 138 Xianlin Rd, Nanjing 210023, Jiangsu, Peoples R China 2.Chinese Acad Sci, Shanghai Inst Mat Med, State Key Lab Drug Res, Drug Discovery & Design Ctr, 555 Zuchongzhi Rd, Shanghai 201203, Peoples R China 3.Univ Chinese Acad Sci, 19A Yuan Rd, Beijing 100049, Peoples R China 4.Chinese Acad Sci, CAS Key Lab Receptor Res, Shanghai Inst Mat Med, 555 Zuchongzhi Rd, Shanghai 201203, Peoples R China 5.Nanjing Univ, State Key Lab Pharmaceut Biotechnol, Nanjing 210023, Jiangsu, Peoples R China
推荐引用方式 GB/T 7714	Fu, Zunyun,Li, Xutong,Wang, Zhaohui,et al. Optimizing chemical reaction conditions using deep learning: a case study for the Suzuki-Miyaura cross-coupling reaction[J]. ORGANIC CHEMISTRY FRONTIERS,2020,7(16):2269-2277.
APA	Fu, Zunyun.,Li, Xutong.,Wang, Zhaohui.,Li, Zhaojun.,Liu, Xiaohong.,...&Zheng, Mingyue.(2020).Optimizing chemical reaction conditions using deep learning: a case study for the Suzuki-Miyaura cross-coupling reaction.ORGANIC CHEMISTRY FRONTIERS,7(16),2269-2277.
MLA	Fu, Zunyun,et al."Optimizing chemical reaction conditions using deep learning: a case study for the Suzuki-Miyaura cross-coupling reaction".ORGANIC CHEMISTRY FRONTIERS 7.16(2020):2269-2277.