Molecular Contrastive Learning with Chemical Element Knowledge Graph


  • Yin Fang Zhejiang University
  • Qiang Zhang Zhejiang University
  • Haihong Yang Zhejiang University
  • Xiang Zhuang Zhejiang University
  • Shumin Deng Zhejiang University
  • Wen Zhang Zhejiang University
  • Ming Qin Zhejiang University
  • Zhuo Chen Zhejiang University
  • Xiaohui Fan Zhejiang University
  • Huajun Chen Zhejiang University



Data Mining & Knowledge Management (DMKM)


Molecular representation learning contributes to multiple downstream tasks such as molecular property prediction and drug design. To properly represent molecules, graph contrastive learning is a promising paradigm as it utilizes self-supervision signals and has no requirements for human annotations. However, prior works fail to incorporate fundamental domain knowledge into graph semantics and thus ignore the correlations between atoms that have common attributes but are not directly connected by bonds. To address these issues, we construct a Chemical Element Knowledge Graph (KG) to summarize microscopic associations between elements and propose a novel Knowledge-enhanced Contrastive Learning (KCL) framework for molecular representation learning. KCL framework consists of three modules. The first module, knowledge-guided graph augmentation, augments the original molecular graph based on the Chemical Element KG. The second module, knowledge-aware graph representation, extracts molecular representations with a common graph encoder for the original molecular graph and a Knowledge-aware Message Passing Neural Network (KMPNN) to encode complex information in the augmented molecular graph. The final module is a contrastive objective, where we maximize agreement between these two views of molecular graphs. Extensive experiments demonstrated that KCL obtained superior performances against state-of-the-art baselines on eight molecular datasets. Visualization experiments properly interpret what KCL has learned from atoms and attributes in the augmented molecular graphs.




How to Cite

Fang, Y., Zhang, Q., Yang, H., Zhuang, X., Deng, S., Zhang, W., Qin, M., Chen, Z., Fan, X., & Chen, H. (2022). Molecular Contrastive Learning with Chemical Element Knowledge Graph. Proceedings of the AAAI Conference on Artificial Intelligence, 36(4), 3968-3976.



AAAI Technical Track on Data Mining and Knowledge Management