Virtual Bonding Enhanced Graph Self-Supervised Learning for Molecular Property Prediction

Abstract

Accurate prediction of molecular properties is essential for modern drug design and discovery. Self-supervised learning (SSL) and Graph Neural Networks (GNNs) have been widely used in this field to learn molecular representations and predict molecular properties. However, previous graph-based deep learning methods have overlooked the important weak interaction, that is, long-range interatomic interaction, which is crucial in determining the molecular properties. This study presents a novel self-supervised learning framework, Virtual Bonding Enhanced Molecular Property Prediction (VIBE-MPP), to address the limitations of existing methods by incorporating weak interactions and 3D spatial information into the molecular representations. VIBE-MPP utilizes a Virtual Bonding Graph Neural Network (VBGNN) to construct a virtual bonding enhanced graph that encodes molecules, and a Dual-level Self-supervised Boosted Pretraining (DSBP) approach to enhance representation learning through four designed pretext tasks. The framework introduces virtual bonds to represent atom interactions within a radius of 10 Å, enabling an atom to engage in message passing with multiple other neighboring atoms simultaneously. The model is evaluated on 10 benchmark datasets, demonstrating superior performance over state-of-the-art methods in both classification and regression tasks. On average, it improves upon the best baseline models by 3.20% and achieves optimal performance on four regression datasets. Additionally, visualizations of the learned molecular representations in downstream datasets show that VIBE-MPP effectively captures molecular properties and semantic information.