IPM: Information Lossless Pre-training Strategy for Molecular Property Prediction

Given the pivotal role of molecular property prediction in drug development and material science, graph self-supervised learning has been implemented in molecular representation learning to compensate for the shortage of labeled molecules. However, current proposed methods often focus on designing data augmentation schemes and leveraging domain knowledge to improve performance, which inevitably leads to molecular semantics loss and limited generalization capability. To the end, we propose IPM, an Information lossless Pretraining strategy for Molecular property prediction that leverages the information of both the original graph and line graph of molecules. Specifically, by contrasting the given graph with the corresponding line graph, the graph encoder can fully learn the generic molecular semantic representation without profound domain knowledge. We also design a new message-passing scheme that retains information consistency during message passing between two kinds of graphs. Additionally, we present two graph contrastive losses for performance fixing and over-smoothing prevention during the learning process. Experimental results on multiple regression tasks for molecular property prediction demonstrate the effectiveness of IPM against state-of-the-art (SOTA) methods.