Contrastive Dual-Interaction Graph Neural Network for Molecular Property Prediction

arXiv:2405.02628v1 Announce Type: new
Abstract: Molecular property prediction is a key component of AI-driven drug discovery and molecular characterization learning. Despite recent advances, existing methods still face challenges such as limited ability to generalize, and inadequate representation of learning from unlabeled data, especially for tasks specific to molecular structures. To address these limitations, we introduce DIG-Mol, a novel self-supervised graph neural network framework for molecular property prediction. This architecture leverages the power of contrast learning with dual interaction mechanisms and …

abstract advances ai-driven arxiv challenges cs.ai cs.lg data discovery drug discovery face graph graph neural network key network neural network prediction property representation tasks type