Abstract: Catalyst design in asymmetric reaction development has traditionally been driven by empiricism, wherein experimentalists attempt to qualitatively recognize structural patterns to improve selectivity. Machine learning algorithms and chemoinformatics can potentially accelerate this process by recognizing otherwise inscrutable patterns in large datasets. Herein we report a computationally guided workflow for chiral catalyst selection using chemoinformatics at every stage of development. Robust molecular descriptors that are agnostic to the catalyst scaffold allow for selection of a universal training set on the basis of steric and electronic properties. This set can be used to train machine learning methods to make highly accurate predictive models over a broad range of selectivity space. Using support vector machines and deep feed-forward neural networks, we demonstrate accurate predictive modeling in the chiral phosphoric acid-catalyzed thiol addition to N-acylimines.

Abstract: Methods, apparatus, and storage medium for determining a combination of coupling partners for a reaction according to input data. The method includes obtaining test input data for a test coupling partner of a test chemical type; obtaining selected input data for a selected coupling partner of a selected chemical type; determining, based on a reaction condition library, a candidate reaction condition set according to the test input data and selected input data, the candidate reaction condition set comprising a previous reaction condition; determining a candidate reaction vector representative of the candidate reaction condition set; inputting the candidate reaction vector into an input layer of a neural network set; and receiving an output at an output layer of the neural network set, the output indicative of a predicted yield from reacting the test coupling partner and the selected coupling partner under the candidate reaction condition set.

Abstract: Catalyst design in asymmetric reaction development has traditionally been driven by empiricism, wherein experimentalists attempt to qualitatively recognize structural patterns to improve selectivity. Machine learning algorithms and chemoinformatics can potentially accelerate this process by recognizing otherwise inscrutable patterns in large datasets. Herein we report a computationally guided workflow for chiral catalyst selection using chemoinformatics at every stage of development. Robust molecular descriptors that are agnostic to the catalyst scaffold allow for selection of a universal training set on the basis of steric and electronic properties. This set can be used to train machine learning methods to make highly accurate predictive models over a broad range of selectivity space. Using support vector machines and deep feed-forward neural networks, we demonstrate accurate predictive modeling in the chiral phosphoric acid-catalyzed thiol addition to N-acylimines.