VHGAE: Drug-Target Interaction Prediction Model Based on Heterogeneous Graph Variational Autoencoder

Identifying drug-target interaction (DTI) is crucial for drug discovery and repositioning. Biological identification of DTI is costly and time-consuming, so heterogeneous network methods for DTI prediction, which can speed up drug development, have drawn much attention. However, the scarcity of know...

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Veröffentlicht in:Interdisciplinary sciences : computational life sciences
Hauptverfasser: Zhang, Chen, Sun, Jiaqi, Xing, Linlin, Zhang, Longbo, Cai, Hongzhen, Che, Kai
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Germany 21.08.2025
Schlagworte:
Drug-target interaction prediction
Heterogeneous graph
Variational inference
Variational graph autoencoder
ISSN:1913-2751, 1867-1462, 1867-1462
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Zusammenfassung:Identifying drug-target interaction (DTI) is crucial for drug discovery and repositioning. Biological identification of DTI is costly and time-consuming, so heterogeneous network methods for DTI prediction, which can speed up drug development, have drawn much attention. However, the scarcity of known drug-target pairs leads to an overwhelmingly sparse network structure. This sparsity makes it difficult for graph convolutional networks to fully capture the potential relationships between nodes during information transmission and feature extraction, thus challenging the prediction performance of DTI. Therefore, we propose a method, called VHGAE, based on a heterogeneous graph variational autoencoder to predict drug-target interactions. The VHGAE model integrates a wealth of prior knowledge from various sources related to drugs and targets to construct a heterogeneous network. To address the sparsity issue within the constructed heterogeneous network, first, the weighted k-nearest neighbor algorithm is used to densify the DTI network to increase the connectivity of the sparse network. Second, the weighted graph convolutional network within a variational graph autoencoder framework is employed to bolster the edge weights with limited connections. Third, a variational expectation maximization algorithm is introduced between the encoder and the decoder to recover potential relationships in the sparse network. The experimental results on two datasets show that the performance of VHGAE outperforms nine state-of-the-art DTI prediction methods, which suggests that the design of VHGAE in multi-source data fusion and sparse network processing plays an important role in improving prediction performance.
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ISSN:1913-2751
1867-1462
1867-1462
DOI:10.1007/s12539-025-00758-8