Consistency-regularized graph neural networks for molecular property prediction.

Although graph neural networks (GNNs) have proven powerful in molecular property prediction tasks, they tend to underperform when trained on small datasets. Conventional data augmentation strategies are generally ineffective in this context, as simply perturbing molecular graphs can unintentionally alter their intrinsic properties. In this study, we propose a consistency-regularized graph neural network (CRGNN) method to better utilize molecular graph augmentation during training. We apply molecular graph augmentation to obtain strongly and weakly-augmented views for each molecular graph. By incorporating a consistency regularization loss into the learning objective, the GNN is encouraged to learn representations such that the strongly-augmented views of a molecular graph are mapped close to a weakly-augmented view of the same graph. In doing so, molecular graph augmentation can contribute to improving the prediction performance of the GNN while mitigating its negative effects. Through experimental evaluation on various molecular benchmark datasets, we demonstrate that the proposed method outperforms existing methods that leverage molecular graph augmentation, especially when the training dataset is smaller.