Feature selection in molecular graph neural networks based on quantum chemical approaches

Feature selection is an important topic that has been widely studied in data science. Recently, graph neural networks (GNNs) and graph convolutional networks (GCNs) have also been employed in chemistry. To enhance the performance characteristics of the GNN and GCN in the field of chemistry, the feature selection should also be discussed in detail from the chemistry viewpoint. Thus, this study proposes a new feature in molecular GNNs based on the quantum chemical approaches and discusses the accuracy, overcorrelation between features, and interpretability. From the overcorrelation and accuracy, the important graph convolution (IGC) with molecular-atomic properties (MAP) proposed herein showed good performance. Moreover, the integrated gradients analysis showed that the machine learning model with the IGC(MAP) explained the prediction outputs reasonably.