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

Research output: Contribution to journal › Article › peer-review

14 Citations (Scopus)

Abstract

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%).

Original language	English
Pages (from-to)	4114-4117
Number of pages	4
Journal	Chemical Communications
Volume	57
Issue number	34
DOIs	https://doi.org/10.1039/d1cc00586c
Publication status	Published - 30 Apr 2021
Externally published	Yes

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title = "A graph-convolutional neural network for addressing small-scale reaction prediction",

abstract = "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\%).",

author = "Yejian Wu and Chengyun Zhang and Ling Wang and Hongliang Duan",

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AU - Wang, Ling

AU - Duan, Hongliang

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AB - 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%).

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

Abstract

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