Issue 20, 2021

Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts

Abstract

Hundreds of catalytic methods are developed each year to meet the demand for high-purity chiral compounds. The computational design of enantioselective organocatalysts remains a significant challenge, as catalysts are typically discovered through experimental screening. Recent advances in combining quantum chemical computations and machine learning (ML) hold great potential to propel the next leap forward in asymmetric catalysis. Within the context of quantum chemical machine learning (QML, or atomistic ML), the ML representations used to encode the three-dimensional structure of molecules and evaluate their similarity cannot easily capture the subtle energy differences that govern enantioselectivity. Here, we present a general strategy for improving molecular representations within an atomistic machine learning model to predict the DFT-computed enantiomeric excess of asymmetric propargylation organocatalysts solely from the structure of catalytic cycle intermediates. Mean absolute errors as low as 0.25 kcal mol−1 were achieved in predictions of the activation energy with respect to DFT computations. By virtue of its design, this strategy is generalisable to other ML models, to experimental data and to any catalytic asymmetric reaction, enabling the rapid screening of structurally diverse organocatalysts from available structural information.

Graphical abstract: Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts

Supplementary files

Article information

Article type
Edge Article
Submitted
26 1 2021
Accepted
01 4 2021
First published
03 4 2021
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY-NC license

Chem. Sci., 2021,12, 6879-6889

Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts

S. Gallarati, R. Fabregat, R. Laplaza, S. Bhattacharjee, M. D. Wodrich and C. Corminboeuf, Chem. Sci., 2021, 12, 6879 DOI: 10.1039/D1SC00482D

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