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%).