Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning

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.

Machine-learned impurity level prediction for semiconductors: the example of Cd-based chalcogenides

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Towards the online computer-aided design of catalytic pockets

“…Finally, the digital version of the steric map, which is the array of points defining the surface in the Cartesian space, could be used as a digital steric descriptor within multilinear regression analysis [59][60][61] or could be embedded in a workflow for the high-throughput screening of new catalysts within machine learning approaches 50,62 .…”

Artificial Intelligence to Power the Future of Materials Science and Engineering

“…After several centuries of development, a large amount of data has been accumulated in the field of materials science . However, the inherent limitations of human cognitive ability make it difficult for human beings to absorb and process the massive literature and data produced every day . Only a small part of data (compared with the whole data volume) can be analyzed in a certain subdivision field.…”