BatmanNet: bi-branch masked graph transformer autoencoder for molecular representation.

ABSTRACT: Although substantial efforts have been made using graph neural networks (GNNs) for artificial intelligence (AI)-driven drug discovery, effective molecular representation learning remains an open challenge, especially in the case of insufficient labeled molecules. Recent studies suggest that big GNN models pre-trained by self-supervised learning on unlabeled datasets enable better transfer performance in downstream molecular property prediction tasks. However, the approaches in these studies require multiple complex self-supervised tasks and large-scale datasets , which are time-consuming, computationally expensive and difficult to pre-train end-to-end. Here, we design a simple yet effective self-supervised strategy to simultaneously learn local and global information about molecules, and f

SUBMITTER: Wang Z 

PROVIDER: S-EPMC10783874 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature