MocGCL: Molecular Graph Contrastive Learning via Negative Selection

Molecular classification benefits a lot from the re-cent success of graph contrastive learning (GCL) which pulls positive samples close and pushes the negative samples apart. GCL methods generate negative and positive samples via graph augmentation. Due to the structural corruption caused by graph augmentation, not all generated negative samples retain discrim-inative semantics. However, existing GCL methods ignore the difference between negative samples and hold an assumption that the importance of all negative samples is the same, leading to degraded performance of molecular classification. To address this issue, in this paper, we propose a novel molecular graph contrastive learning model (MocGCL) by selecting more useful negative samples to improve the performance of molecular classification. Specifically, we first employ different encoders to generate positive samples to improve the diversity of positive samples. Then, we design negative generation to generate negative samples and define semantic integrity to measure the usefulness of generated negative samples. Moreover, we propose the novel negative selection to dynamically select the negative samples of more usefulness to improve the molecular representation. In addition, we improve the contrastive loss to adaptively adjust the distance between selected negative samples, which can pre-serve the distinctive properties of selected negative samples in sample space. Extensive experiments on six typical bioinformatics datasets demonstrate the effectiveness of our MocGCL compared to most state-of-the-art methods.