Molecular Graph Representation Learning via Structural Similarity Information
<|reference_start|>Molecular Graph Representation Learning via Structural Similarity Information: 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 \textbf{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.<|reference_end|>
@article{yao2024molecular,
    title={Molecular Graph Representation Learning via Structural Similarity
  Information},
    author={Chengyu Yao, Hong Huang, Hang Gao, Fengge Wu, Haiming Chen, Junsuo
  Zhao},
    journal={Machine Learning and Knowledge Discovery in Databases. Research
  Track. ECML PKDD 2024. Lecture Notes in Computer Science(), vol 14943.
  Springer, Cham},
    year={2024},
    doi={10.1007/978-3-031-70352-2_21},
    archivePrefix={arXiv},
    eprint={2409.08580},
    primaryClass={cs.LG cs.AI}
}