KGG: Knowledge-Guided Graph Self-Supervised Learning to Enhance Molecular Property Predictions.

ABSTRACT: Molecular property prediction has become essential in accelerating advancements in drug discovery and materials science. Graph Neural Networks have recently demonstrated remarkable success in molecular representation learning; however, their broader adoption is impeded by two significant challenges: (1) data scarcity and constrained model generalization due to the expensive and time-consuming task of acquiring labeled data and (2) inadequate initial node and edge features that fail to incorporate comprehensive chemical domain knowledge, notably orbital information. To address these limitations, we introduce a Knowledge-Guided Graph (KGG) framework employing self-supervised learning to pretrain models using orbital-level features in order to mitigate reliance on extensive labeled data sets.

SUBMITTER: To VT 

PROVIDER: S-EPMC12458709 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature