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Abstract
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%).
Supplementary materials
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A graph-convolutional neural network for addressing small-scale reaction prediction
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