Optimizing chemical reaction conditions using deep learning: a case study for the Suzuki-Miyaura cross-coupling reaction
Optimizing chemical reaction conditions using deep learning: a case study for the Suzuki-Miyaura cross-coupling reaction
复制标题
使用深度学习优化化学反应条件：铃木-宫浦交叉偶联反应的案例研究
DOI：
10.1039/d0qo00544d
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发表时间：
2020-08-21
影响因子：
5.4
通讯作者：
Zheng, Mingyue
中科院分区：
文献类型：
--
作者：
Fu, Zunyun;Li, Xutong;Zheng, Mingyue
在这里，我们报告了一个深度学习模型的可行性研究，用于探索给定化学反应的最佳反应条件。基于高质量的现有实验数据，训练模型学习化学环境、反应条件和产物产率之间的关系，然后通过探索可达的反应空间，对未见的反应进行合理的外推。将该策略应用于Suzuki-Miyaura交叉偶联反应中，以寻找给定反应物的最佳催化剂，同时发现反应条件的最佳组合。我们证明了训练模型能够确定生产催化剂以及最有利的催化剂负载和反应温度，无论是模拟反应还是外部看不见的反应。这项工作旨在深入了解在化学反应条件优化中引入深度学习方法的可行性。
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.