Providing direction for mechanistic inferences in radical cascade cyclization using a Transformer model-Reference-Cited by-同舟云学术

Providing direction for mechanistic inferences in radical cascade cyclization using a Transformer model

Published:2022 Issue:9 Volume:9 Page:2498-2508
ISSN:2052-4129
Container-title:Organic Chemistry Frontiers
language:en
Short-container-title:Org. Chem. Front.

Author:

Xu Jiangcheng¹²^ORCID,Zhang Yun¹^ORCID,Han Jiale¹,Su An³,Qiao Haoran⁴,Zhang Chengyun¹,Tang Jing¹,Shen Xi¹,Sun Bin¹,Yu Wenbo²,Zhai Silong¹,Wang Xinqiao¹,Wu Yejian¹^ORCID,Su Weike¹^ORCID,Duan Hongliang¹

Affiliation:

1. Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences; Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Zhejiang University of Technology, Hangzhou, 310014, P. R. China

2. Hangzhou Vocational & Technical College, Hangzhou, 310014, P. R. China

3. College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, P. R. China

4. College of Mathematics and Physics, Shanghai University of Electric Power, Shanghai, 201203, P. R. China

Abstract

Transformer, a sequence-to-sequence deep learning model, is capable of predicting the reaction intermediates of radical cascade cyclization. This study provides a novel approach to help chemists discover the mechanisms of organic reactions.

Funder

National Natural Science Foundation of China

Natural Science Foundation of Zhejiang Province

Publisher

Royal Society of Chemistry (RSC)

Subject

Organic Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2022/QO/D2QO00188H

Reference77 articles.

1. The fingerprints of reaction mechanisms

2. Mechanisms and Substituent Effects of Metal-Free Bioorthogonal Reactions