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A Graph-Convolutional Neural Network for Addressing Small-Scale Reaction Prediction

preprint
submitted on 31.01.2021, 03:36 and posted on 02.02.2021, 06:27 by Yejian Wu, Chengyun Zhang, Ling Wang, Hongliang Duan
We describe a graph-convolutional neural network (GCN) model whose reaction prediction capable as potent as the transformer model on sufficient data, and adopt the Baeyer-Villiger oxidation to explore their performance differences on limited data. The top-1 accuracy of GCN model (90.4%) is higher than that of transformer model (58.4%).

Funding

National Natural Science Foundation of China, NSFC (Grant NO.81903438)

History

Email Address of Submitting Author

hduan@zjut.edu.cn

Institution

Zhejiang University of Technology

Country

China

ORCID For Submitting Author

0000-0002-9194-0115

Declaration of Conflict of Interest

No conflict of interest

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