A graph-convolutional neural network for addressing small-scale reaction prediction

Yejian Wu; Chengyun Zhang; Ling Wang; Hongliang Duan

doi:10.1039/d1cc00586c

A graph-convolutional neural network for addressing small-scale reaction prediction

Yejian Wu
, Chengyun Zhang
, Ling Wang
, Hongliang Duan

Zhejiang University of Technology

研究成果: Article › 同行評審

14 引文斯高帕斯（Scopus）

摘要

We describe a graph-convolutional neural network (GCN) model, the reaction prediction capabilities of which are as potent as those of the transformer model based on sufficient data, and we adopt the Baeyer-Villiger oxidation reaction to explore their performance differences based on limited data. The top-1 accuracy of the GCN model (90.4%) is higher than that of the transformer model (58.4%).

原文	English
頁（從 - 到）	4114-4117
頁數	4
期刊	Chemical Communications
卷	57
發行號	34
DOIs	https://doi.org/10.1039/d1cc00586c
出版狀態	Published - 30 4月 2021
對外發佈	是

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A graph-convolutional neural network for addressing small-scale reaction prediction

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