Q-GEM: Quantum Chemistry Knowledge Fusion Geometry-Enhanced Molecular Representation for Property Prediction

Recently, various self-supervised learning (SSL) methods based on 3D graph neural networks (GNNs) have been developed to comprehensively represent the structural information of molecules in 3D space; this is essential for discovering new drugs. However, existing methods fail to comprehensively characterize the 3D structures of molecules and neglect the electronic structural information that significantly influences key properties such as molecular reactivity, strong electrostatic interactions, and chemical adsorption. Therefore, here, a novel molecular representation learning method is constructed, Q-GEM, incorporating quantum and geometric structural information enhancement, based on the quantum chemical property database QuanDB and SSL methods. Q-GEM comprises a GNN embedded with the molecular electronic and complete 3D geometrical structural information as well as several well-designed multiscale SSL tasks, achieving superior absolute molecular conformation prediction and conformational discrimination. The Q-GEM achieved state-of-the-art performance in 12 out of 13 prediction tasks on the MoleculeNet dataset, with an average performance improvement of 3.3% and 2.0% for classification and regression prediction tasks, respectively. Moreover, an average performance improvement of 5.2% is achieved in three localized quantum chemical properties, fully demonstrating the excellent performance of Q-GEM in distinguishing molecular electronic structures. The Q-GEM represents a novel, powerful breakthrough for accurate molecular property prediction.