Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations

Molecule representation learning underpins diverse downstream applications such as molecular property and side effect understanding and prediction. In this paper, we recognize the two-level structure of individual molecule as having intrinsic graph structure as well as being a node in a large molecule knowledge graph, and present GODE, a new approach that seamlessly integrates graph representations of individual molecules with multi-domain biomedical data from knowledge graphs. By pre-training two graph neural networks (GNNs) on different graph structures, combined with contrastive learning, GODE adeptly fuses molecular structures with their corresponding knowledge graph substructures. This fusion results in a more robust and informative representation, enhancing molecular property prediction by harnessing both chemical and biological information. Finetuned on 11 chemical property tasks, our model surpasses benchmarks, achieving an average ROC-AUC improvement of 14.5%, 9.8%, and 7.3% on BBBP, SIDER, and Tox21 datasets. In regression tasks on ESOL and QM7 datasets, we achieve average improvements of 21.0% and 29.6% improvements in RMSE and MAE, setting a new field benchmark.