ABT-MPNN: an atom-bond transformer-based message-passing neural network for molecular property prediction.

ABSTRACT: Graph convolutional neural networks (GCNs) have been repeatedly shown to have robust capacities for modeling graph data such as small molecules. Message-passing neural networks (MPNNs), a group of GCN variants that can learn and aggregate local information of molecules through iterative message-passing iterations, have exhibited advancements in molecular modeling and property prediction. Moreover, given the merits of Transformers in multiple artificial intelligence domains, it is desirable to combine the self-attention mechanism with MPNNs for better molecular representation. We propose an atom-bond transformer-based message-passing neural network (ABT-MPNN), to improve the molecular representation embedding process for molecular property predictions. By designing corresponding attention m

SUBMITTER: Liu C 

PROVIDER: S-EPMC9968697 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature