Hierarchical Molecular Graph Self-Supervised Learning for property prediction.

ABSTRACT: Molecular graph representation learning has shown considerable strength in molecular analysis and drug discovery. Due to the difficulty of obtaining molecular property labels, pre-training models based on self-supervised learning has become increasingly popular in molecular representation learning. Notably, Graph Neural Networks (GNN) are employed as the backbones to encode implicit representations of molecules in most existing works. However, vanilla GNN encoders ignore chemical structural information and functions implied in molecular motifs, and obtaining the graph-level representation via the READOUT function hinders the interaction of graph and node representations. In this paper, we propose Hierarchical Molecular Graph Self-supervised Learning (HiMol), which introduces a pre-training

SUBMITTER: Zang X 

PROVIDER: S-EPMC9938270 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature