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Can I read Predicting Highly Enantioselective Catalysts Using Tunable Fragment Descriptors** on EtoBox?
Predicting Highly Enantioselective Catalysts Using Tunable Fragment Descriptors** by Nobuya Tsuji; Pavel Sidorov; Chendan Zhu; Yuuya Nagata; Timur Gimadiev; Alexandre Varnek; Benjamin List is a Chemistry article available to read on EtoBox.
What is Predicting Highly Enantioselective Catalysts Using Tunable Fragment Descriptors** about?
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 timeand 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.
Who reads Predicting Highly Enantioselective Catalysts Using Tunable Fragment Descriptors**?
It is typically read by researchers, students, and practitioners in Chemistry.
- Author
- Nobuya Tsuji; Pavel Sidorov; Chendan Zhu; Yuuya Nagata; Timur Gimadiev; Alexandre Varnek; Benjamin List
- Publisher
- Wiley
- Published
- 2023
- Language
- EN
- Field
- Chemistry (Physical Sciences)