Design of Experimental Conditions with Machine Learning for Collaborative Organic Synthesis Reactions Using Transition-Metal Catalysts.

ABSTRACT: To improve product yields in synthetic reactions, it is important to use appropriate catalysts. In this study, we used machine learning to design catalysts for a reaction system in which both Buchwald-Hartwig-type and Suzuki-Miyaura-type cross-coupling reactions proceed simultaneously. First, using an existing dataset, yield prediction models were constructed with machine learning between experimental conditions, including the substrate and catalyst and the yields of the two products. Seven methods for calculating both the substrate and catalyst descriptors were proposed, and the predictive ability of the yield prediction models was discussed in terms of the descriptors and machine learning methods. Then, the constructed models were used to predict the compound yields for new combinations

SUBMITTER: Ebi T 

PROVIDER: S-EPMC8529890 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature