Multi-View Graph Neural Networks for Molecular Property Prediction

TL;DR

This paper introduces Multi-View Graph Neural Networks (MV-GNN) for molecular property prediction, leveraging atom and bond information with enhanced interpretability and expressiveness, achieving superior results on benchmark datasets.

Contribution

The paper proposes a novel multi-view GNN architecture with a shared self-attentive readout and disagreement loss, plus a cross-dependent message passing scheme, improving molecular property prediction.

Findings

MV-GNN outperforms state-of-the-art models on benchmarks.

The model's interpretability aligns with chemical knowledge.

Theoretical analysis confirms the model's expressiveness.

Abstract

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.