  Molecule representation learning is crucial for various downstream
applications, such as understanding and predicting molecular properties and
side effects. In this paper, we propose a novel method called GODE, which takes
into account the two-level structure of individual molecules. We recognize that
molecules have an intrinsic graph structure as well as being a node in a larger
molecule knowledge graph. GODE integrates graph representations of individual
molecules with multidomain biochemical data from knowledge graphs. By
pre-training two graph neural networks (GNNs) on different graph structures,
combined with contrastive learning, GODE fuses molecular structures with their
corresponding knowledge graph substructures. This fusion results in a more
robust and informative representation, which enhances molecular property
prediction by harnessing both chemical and biological information. When
fine-tuned across 11 chemical property tasks, our model outperforms existing
benchmarks, registering an average ROC-AUC uplift of 13.8% for classification
tasks and an average RMSE/MAE enhancement of 35.1% for regression tasks.
Impressively, it surpasses the current leading model in molecule property
predictions with average advancements of 2.1% in classification and 6.4% in
regression tasks.
