Knowledge-aware Contrastive Molecular Graph Learning

• URL: http://arxiv.org/abs/2103.13047v1
• Date: Wed, 24 Mar 2021 08:55:08 GMT
• Title: Knowledge-aware Contrastive Molecular Graph Learning
• Authors: Yin Fang, Haihong Yang, Xiang Zhuang, Xin Shao, Xiaohui Fan and Huajun Chen
• Abstract summary: We propose Contrastive Knowledge-aware GNN (CKGNN) for self-supervised molecular representation learning. We explicitly encode domain knowledge via knowledge-aware molecular encoder under the contrastive learning framework. Experiments on 8 public datasets demonstrate the effectiveness of our model with a 6% absolute improvement on average.
• Score: 5.08771973600915
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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