MolNet: A Chemically Intuitive Graph Neural Network for Prediction of Molecular Properties.

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Abstract

Most graph neural networks (GNNs) in deep-learning chemistry collect and update atom and molecule features from the fed atom (and, in some cases, bond) features, basically based on the two-dimensional (2D) graph representation of 3D molecules. However, the 2D-based models do not faithfully represent 3D molecules and their physicochemical properties, exemplified by the overlooked field effect that is a “through-space” effect, not a “through-bond” effect. We propose a GNN model, denoted as MolNet, which accomodates the 3D non-bond information in a molecule, via a noncovalent adjacency matrix [[EQUATION]] , and also bond-strength information from a weighted bond matrix [[EQUATION]] . Comparative studies show that MolNet outperforms various baseline GNN models and gives a state-of-the-art performance in the clsssification task of BACE dataset and regression task of ESOL dataset. This work suggests a future direction for the construction of deep-learning models that are chemically intuitive and compatible with the existing chemistry concepts and tools.© 2022 Wiley-VCH GmbH.