Recently many efforts have been devoted to applying graph neural networks (GNNs) molecular property prediction which is a fundamental task for computational drug and material discovery. One of major obstacles hinder the successful by GNNs scarcity labeled data. Though contrastive learning (GCL) methods achieved extraordinary performance with insufficient data, most focused on designing data aug...

Abstract The ability of molecular property prediction is great significance to drug discovery, human health, and environmental protection. Despite considerable efforts, quantitative various properties remains a challenge. Although some machine learning models, such as bidirectional encoder from transformer, can incorporate massive unlabeled data into representations via self-supervised strategy...