  The crux of molecular property prediction is to generate meaningful
representations of the molecules. One promising route is to exploit the
molecular graph structure through Graph Neural Networks (GNNs). It is well
known that both atoms and bonds significantly affect the chemical properties of
a molecule, so an expressive model shall be able to exploit both node (atom)
and edge (bond) information simultaneously. Guided by this observation, we
present Multi-View Graph Neural Network (MV-GNN), a multi-view message passing
architecture to enable more accurate predictions of molecular properties. In
MV-GNN, we introduce a shared self-attentive readout component and disagreement
loss to stabilize the training process. This readout component also renders the
whole architecture interpretable. We further boost the expressive power of
MV-GNN by proposing a cross-dependent message passing scheme that enhances
information communication of the two views, which results in the MV-GNN^cross
variant. Lastly, we theoretically justify the expressiveness of the two
proposed models in terms of distinguishing non-isomorphism graphs. Extensive
experiments demonstrate that MV-GNN models achieve remarkably superior
performance over the state-of-the-art models on a variety of challenging
benchmarks. Meanwhile, visualization results of the node importance are
consistent with prior knowledge, which confirms the interpretability power of
MV-GNN models.
