DIG-Mol: A Contrastive Dual-Interaction Graph Neural Network for Molecular Property Prediction

This paper develops a graph contrastive learning framework for molecular property prediction, incorporating dual interaction and momentum distillation to improve representation learning from complex molecular graphs.

IEEE Journal of Biomedical and Health Informatics, 29(3), 1735-1746