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

文献类型：期刊论文

作者Fu, Zunyun1,2; Li, Xutong2,3; Wang, Zhaohui4; Li, Zhaojun2; Liu, Xiaohong2; Wu, Xiaolong2; Zhao, Jihui2,3; Ding, Xiaoyu2,3; Wan, Xiaozhe2,3; Zhong, Feisheng2,3
刊名ORGANIC CHEMISTRY FRONTIERS
出版日期2020-08-21
卷号7期号:16页码:2269-2277
ISSN号2052-4129
DOI10.1039/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.
WOS关键词CATALYST ; SELECTION ; OPTIMIZATION
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研究方向Chemistry
语种英语
WOS记录号WOS:000558942300013
出版者ROYAL SOC CHEMISTRY
源URL[http://119.78.100.183/handle/2S10ELR8/292300]  
专题新药研究国家重点实验室
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.

入库方式： OAI收割

来源：上海药物研究所

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