Abstract
Graph Neural Networks (GNNs) have been widely employed for feature representation learning in molecular graphs. Therefore, it is crucial to enhance the expressiveness of feature representation to ensure the effectiveness of GNNs. However, a significant portion of current research primarily focuses on the structural features within individual molecules, often overlooking the structural similarity between molecules, which is a crucial aspect encapsulating rich information on the relationship between molecular properties and structural characteristics. Thus, these approaches fail to capture the rich semantic information at the molecular structure level. To bridge this gap, we introduce the Molecular Structural Similarity Motif GNN (MSSM-GNN), a novel molecular graph representation learning method that can capture structural similarity information among molecules from a global perspective. In particular, we propose a specially designed graph that leverages graph kernel algorithms to represent the similarity between molecules quantitatively. Subsequently, we employ GNNs to learn feature representations from molecular graphs, aiming to enhance the accuracy of property prediction by incorporating additional molecular representation information. Finally, through a series of experiments conducted on both small-scale and large-scale molecular datasets, we demonstrate that our model consistently outperforms eleven state-of-the-art baselines. The codes are available at https://github.com/yaoyao-yaoyao-cell/MSSM-GNN.
C. Yao and H. Huang—These authors contributed equally to this work.
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Notes
- 1.
Breaking retrosynthetically interesting chemical substructures [10].
- 2.
A six-membered ring compound containing two nitrogen atoms.
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Acknowledgments
The authors would like to thank the editors and reviewers for their valuable comments. This work is supported by the CAS Project for Young Scientists in Basic Research (Grant No. YSBR-040) and the National Natural Science Foundation of China (Grant No. 62372439), the Natural Science Foundation of Beijing, China (Grant No. 4232038), the Basic Research Program of ISCAS (Grant No. ISCAS-JCMS-202307).
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Yao, C., Huang, H., Gao, H., Wu, F., Chen, H., Zhao, J. (2024). Molecular Graph Representation Learning via Structural Similarity Information. In: Bifet, A., Davis, J., Krilavičius, T., Kull, M., Ntoutsi, E., Žliobaitė, I. (eds) Machine Learning and Knowledge Discovery in Databases. Research Track. ECML PKDD 2024. Lecture Notes in Computer Science(), vol 14943. Springer, Cham. https://doi.org/10.1007/978-3-031-70352-2_21
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