Theoretical and Computational Chemistry

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

Authors

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%).

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Supplementary material

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