  Leveraging domain knowledge including fingerprints and functional groups in
molecular representation learning is crucial for chemical property prediction
and drug discovery. When modeling the relation between graph structure and
molecular properties implicitly, existing works can hardly capture structural
or property changes and complex structure, with much smaller atom vocabulary
and highly frequent atoms. In this paper, we propose the Contrastive
Knowledge-aware GNN (CKGNN) for self-supervised molecular representation
learning to fuse domain knowledge into molecular graph representation. We
explicitly encode domain knowledge via knowledge-aware molecular encoder under
the contrastive learning framework, ensuring that the generated molecular
embeddings equipped with chemical domain knowledge to distinguish molecules
with similar chemical formula but dissimilar functions. Extensive experiments
on 8 public datasets demonstrate the effectiveness of our model with a 6\%
absolute improvement on average against strong competitors. Ablation study and
further investigation also verify the best of both worlds: incorporation of
chemical domain knowledge into self-supervised learning.
