Dual Message Passing Neural Network for Molecular Property Prediction

The crux of molecular property prediction is to conduct meaningful and informative representations of molecules. One promising way of doing this is exploiting the molecular graph structure, which leads to several graph-based deep learning models, such as Graph Neural Networks (GNN), Message Passing Networks (MPN), etc. However, current deep graph learning models generally focus on either node embedding or edge embedding. Since both atoms and chemical bonds play important roles in chemical reactions and reflect the property of molecules, existing methods fail to exploit both node (atom) and edge (bond) features simultaneously to make property prediction. In this paper, we propose Dual Message Passing Neural Network (DualMPNN), a multi-view message passing architecture with a two-tier constraint, to make more accurate molecular property prediction. DualMPNN consists of two sub-models: one model as NodeMPN for atom-oriented modeling and the other as EdgeMPN for bond-oriented modeling. We design a shared self-attentive readout and disagreement loss to stabilize the training process and enhance the interactions between two sub-models. On the other hand, the designed self-attentive readout also provides the interpretability for molecular property prediction, which is crucial for real applications like molecular design and drug discovery. Our extensive experimental evaluations demonstrate that DualMPNN achieves remarkably superior improvement over the state-of-the-art approaches on a variety of challenging benchmarks. Meanwhile, the visualization of the self-attention demonstrates how different atoms affect the prediction performance, which brings in the interpretability of deep learning models out of the black-box.