Molecular substructure graph attention network for molecular property identification in drug discovery

Molecular machine learning based on graph neural network has a broad prospect in molecular property identification in drug discovery. Molecules contain many types of substructures that may affect their properties. However, conventional methods based on graph neural networks only consider the interaction information between nodes, which may lead to the oversmoothing problem in the multi-hop operations. These methods may not efficiently express the interacting information between molecular substructures. Hence, We develop a Molecular SubStructure Graph ATtention (MSSGAT) network to capture the interacting substructural information, which constructs a composite molecular representation with multi-substructural feature extraction and processes such features effectively with a nested convolution plus readout scheme. We evaluate the performance of our model on 13 benchmark data sets, in which 9 data sets are from the ChEMBL data base and 4 are the SIDER, BBBP, BACE, and HIV data sets. Extensive experimental results show that MSSGAT achieves the best results on most of the data sets compared with other state-of-the-art methods.