Graph Matching Based Graph Self-Supervised Learning for Molecular Property Prediction

In the field of graph representation learning for molecular property prediction, self-supervised learning is highly appreciated for its ability to mine potential connections in unlabeled data. However, existing approaches such as neighborhood prediction or contrastive learning often lack in fusing structural and semantic features of molecular graphs. To address this issue, we propose a Graph Matching Based Graph Self-Supervised Learning (GMSSL) framework, which aims to comprehensively learn the structure and semantics of molecular graphs. Specifically, utilizing intra-graph and inter-graph convolution and graph matching methods, GMSSL is able to encode complex information for efficient training without supervision. After pre-training and fine-tuning, GMSSL demonstrates powerful molecular property prediction on multiple datasets.