Before revision:  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.
After revision:  In this paper, we propose Dual Message Passing Neural Network ( DualMPNN ), a multi-view message passing architecture to enable more accurate predictions of molecular properties. In MV-GNN, we introduce a shared self-attentive readout and disagreement loss to stabilize the training process and enhance the interactions between two sub-models.