TSNet: predicting transition state structures with tensor field networks and transfer learning-Reference-Cited by-同舟云学术

TSNet: predicting transition state structures with tensor field networks and transfer learning

Published:2021 Issue:29 Volume:12 Page:10022-10040
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Jackson Riley¹²³,Zhang Wenyuan¹²³,Pearson Jason¹²³^ORCID

Affiliation:

1. Department of Chemistry

2. University of Prince Edward Island

3. Canada

Abstract

Transition states are among the most important molecular structures in chemistry, critical to a variety of fields such as reaction kinetics, catalyst design, and the study of protein function.

Funder

Natural Sciences and Engineering Research Council of Canada

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2021/SC/D1SC01206A

Reference85 articles.

1. Deep learning

2. Bitterless guaifenesin prodrugs-design, synthesis, characterization, in vitro kinetics, and bitterness studies

3. A structurally heterogeneous transition state underlies coupled binding and folding of disordered proteins

4. Exploiting non-covalent π interactions for catalyst design

5. Transition State Modeling and Catalyst Design for Hydrogen Bond-Stabilized Enolate Formation

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