Bond-Aware Molecular Graph Learning With Multi-Graph Interleaved Message Passing.

Graph neural networks (GNNs) have demonstrated remarkable capabilities in molecular property prediction. Existing approaches adopt GNNs by modeling molecules as homogeneous graphs. However, the bonds between atoms can be heterogeneous, whose characterization and role in molecular graph representation learning remain unexplored. To address the heterogeneity issue inherent in molecular graphs, in this work, we build the bond-centric graphs and propose a novel multi-graph learning model, which captures the bond heterogeneity via augmented bond graph view and bond coding for atom features. Different from conventional multi-view learning that focus on late-stage view fusion, our method integrates cross-graph information during the node representation learning phase. Towards this end, we introduce the interleaved message passing graph neural network (IMPGNN), allowing the messages passing across three views of the molecular graph. Moreover, we introduce a novel structure-aware pooling mechanims for graph representation, which yields up to 45.7% gains over simple sum pooling. Comparative experiments on two standard molecular property prediction tasks reveal that our method surpasses all competing approaches (including multimodal models) on 75% of the evaluated benchmark datasets.