Predicting the Feasibility of Copper(I)-Catalyzed Alkyne–Azide Cycloaddition Reactions Using a Recurrent Neural Network with a Self-Attention Mechanism-Reference-Cited by-同舟云学术

Predicting the Feasibility of Copper(I)-Catalyzed Alkyne–Azide Cycloaddition Reactions Using a Recurrent Neural Network with a Self-Attention Mechanism

Published:2020-02-03 Issue:3 Volume:60 Page:1165-1174
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Su Shimin¹^ORCID,Yang Yuyao¹^ORCID,Gan Hanlin²,Zheng Shuangjia¹^ORCID,Gu Fenglong²,Zhao Chao¹,Xu Jun¹^ORCID

Affiliation:

1. Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-Sen University, 132 East Circle at University City, Guangzhou 510006, China

2. Key Laboratory of Theoretical Chemistry of Environment, Ministry of Education, School of Chemistry and Environment, South China Normal University, South Circle at University City, Guangzhou 510006, China

Funder

Ministry of Science and Technology of the People's Republic of China

National Natural Science Foundation of China

Science and Technology Program of Guangzhou

Key Laboratory of New Drug Design and Evaluation, Sun Yat-sen University

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.9b00929

Reference69 articles.