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https://hdl.handle.net/2115/88658

Predicting Highly Enantioselective Catalysts Using Tunable Fragment Descriptors

英語

Organocatalysis

2023-01-23

Wiley-Blackwell

Angewandte chemie-international edition

62(11)

e202218659

Catalyst optimization processes typically rely on inductive and qualitative assumptions of chemists based on screening data. While machine learning models using molecular properties or calculated 3D structures enable quantitative data evaluation, costly quantum chemical calculations are often required. In contrast, readily available binary fingerprint descriptors are time- and cost-efficient, but their predictive performance remains insufficient. Here, we describe a machine learning model based on fragment descriptors, which are fine-tuned for asymmetric catalysis and represent cyclic or polyaromatic hydrocarbons, enabling robust and efficient virtual screening. Using training data with only moderate selectivities, we designed theoretically and validated experimentally new catalysts showing higher selectivities in a challenging asymmetric tetrahydropyran synthesis.

学術雑誌論文

Version of Record

1433-7851