Catalysis

Development and Validation of a Chemoinformatic Workflow for Predicting Reaction Yield for Pd-Catalyzed C-N Couplings with Substrate Generalizability

Authors

Abstract

A machine learning-based tool that provides conditions and predicted yields for Buchwald-Hartwig couplings from a ChemDraw™ structure input is described. The tool is built on an in-house generated experimental dataset that explores a diverse network of reactant pairings. To minimize the number of experiments necessary to produce models and maximize data value, a workflow based on unsupervised machine leaning tools was created. The workflow enables the construction of models which can successfully generalize—making predictions for reactants which are not represented in the dataset.

Content

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