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Metapath-aggregated Heterogeneous Graph Neural Network for Drug–target Interaction Prediction by Mei Li; Xiangrui Cai; Sihan Xu; Hua Ji is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

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Abstract Drug–target interaction (DTI) prediction is an essential step in drug repositioning. A few graph neural network (GNN)-based methods have been proposed for DTI prediction using heterogeneous biological data. However, existing GNN-based methods only aggregate information from directly connected nodes restricted in a drug-related or a target-related network and are incapable of capturing high-order dependencies in the biological heterogeneous graph. In this paper, we propose a metapath-aggregated heterogeneous graph neural network (MHGNN) to capture complex structures and rich semantics in the biological heterogeneous graph for DTI prediction. Specifically, MHGNN enhances heterogeneous graph structure learning and high-order semantics learning by modeling high-order relations via metapaths. Additionally, MHGNN enriches high-order correlations between drug-target pairs (DTPs) by constructing a DTP correlation graph with DTPs as nodes. We conduct extensive experiments on three biological heterogeneous datasets. MHGNN favorably surpasses 17 state-of-the-art methods over 6 evaluation metrics, which verifies its efficacy for DTI prediction. The code is available at https://github.

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Author

Mei Li; Xiangrui Cai; Sihan Xu; Hua Ji

Published

2023

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)