Multi-Instance Learning Approach to the Modeling of Enantioselectivity of Conformationally Flexible Organic Catalysts-Reference-Cited by-同舟云学术

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Multi-Instance Learning Approach to the Modeling of Enantioselectivity of Conformationally Flexible Organic Catalysts

Published:2023-10-30 Issue:21 Volume:63 Page:6629-6641
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Zankov Dmitry¹²^ORCID,Madzhidov Timur³,Polishchuk Pavel⁴^ORCID,Sidorov Pavel²,Varnek Alexandre¹²^ORCID

Affiliation:

1. Laboratory of Chemoinformatics, University of Strasbourg, Strasbourg 67081, France

2. Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Sapporo 001-0021, Japan

3. Chemistry Solutions, Elsevier Ltd., Oxford OX5 1GB, United Kingdom

4. Institute of Molecular and Translational Medicine, Palacký University, Olomouc 77900, Czech Republic

Funder

European Regional Development Fund

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.3c00393

Reference40 articles.

1. New Strategies for Organic Catalysis: The First Enantioselective Organocatalytic 1,3-Dipolar Cycloaddition

2. Proline-Catalyzed Direct Asymmetric Aldol Reactions

3. Asymmetric organocatalysis: an enabling technology for medicinal chemistry

4. Quantitative Structure–Selectivity Relationships in Enantioselective Catalysis: Past, Present, and Future

5. Steric Influences on the Selectivity in Palladium-Catalyzed Allylation

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Catalysing (organo-)catalysis: Trends in the application of machine learning to enantioselective organocatalysis;Beilstein Journal of Organic Chemistry;2024-09-10

2. A Primer on 2D Descriptors in Selectivity Modeling for Asymmetric Catalysis;Chemistry – A European Journal;2023-12-19

3. Chemical complexity challenge: Is multi‐instance machine learning a solution?;WIREs Computational Molecular Science;2023-11-27

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