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Medientyp:
E-Artikel
Titel:
MolNet: A Chemically Intuitive Graph Neural Network for Prediction of Molecular Properties
Beteiligte:
Kim, Yeji;
Jeong, Yoonho;
Kim, Jihoo;
Lee, Eok Kyun;
Kim, Won June;
Choi, Insung S.
Erschienen:
Wiley, 2022
Erschienen in:
Chemistry – An Asian Journal, 17 (2022) 16
Sprache:
Englisch
DOI:
10.1002/asia.202200269
ISSN:
1861-4728;
1861-471X
Entstehung:
Anmerkungen:
Beschreibung:
AbstractMost 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 accommodates the 3D non‐bond information in a molecule, via a noncovalent adjacency matrix, and also bond‐strength information from a weighted bond matrix. Comparative studies show that MolNet outperforms various baseline GNN models and gives a state‐of‐the‐art performance in the classification 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.