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Predicting Highly Enantioselective Catalysts Using Tunable Fragment Descriptors

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Title: Predicting Highly Enantioselective Catalysts Using Tunable Fragment Descriptors
Authors: Tsuji, Nobuya Browse this author →KAKEN DB
Sidorov, Pavel Browse this author →KAKEN DB
Zhu, Chendan Browse this author
Nagata, Yuuya Browse this author →KAKEN DB
Gimadiev, Timur Browse this author →KAKEN DB
Varnek, Alexandre Browse this author
List, Benjamin Browse this author →KAKEN DB
Keywords: Asymmetric Catalysis
Machine Learning
Organocatalysis
Issue Date: 23-Jan-2023
Publisher: Wiley-Blackwell
Journal Title: Angewandte chemie-international edition
Volume: 62
Issue: 11
Start Page: e202218659
Publisher DOI: 10.1002/anie.202218659
Abstract: 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.
Type: article
URI: http://hdl.handle.net/2115/88658
Appears in Collections:化学反応創成研究拠点:ICReDD (Institute for Chemical Reaction Design and Discovery : ICReDD) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

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