GIAE-DTI: Predicting Drug-Target Interactions Based on Heterogeneous Network and GIN-Based Graph Autoencoder

Accurate prediction of drug-target interactions (DTIs) is essential for advancing drug discovery and repurposing. However, the sparsity of DTI data limits the effectiveness of existing computational methods, which primarily focus on sparse DTI networks and have poor performance in aggregating inform...

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Bibliographic Details
Published in:IEEE journal of biomedical and health informatics Vol. 29; no. 11; pp. 7896 - 7909
Main Authors: Wang, Mengdi, Lei, Xiujuan, Liu, Lian, Chen, Jianrui, Wu, Fang-Xiang
Format: Journal Article
Language:English
Published: United States IEEE 01.11.2025
Subjects:
Algorithms
Autoencoder
Computational Biology - methods
Deep Learning
Diffusion tensor imaging
Diseases
Drug Discovery - methods
Drug Interactions
Drug Repositioning - methods
Drug-target interactions
Drugs
Feature extraction
graph auto-encoder
graph isomorphism network
heterogeneous network
Heterogeneous networks
Humans
Neural Networks, Computer
Proteins
Vectors
ISSN:2168-2194, 2168-2208, 2168-2208
Online Access:Get full text
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Summary:Accurate prediction of drug-target interactions (DTIs) is essential for advancing drug discovery and repurposing. However, the sparsity of DTI data limits the effectiveness of existing computational methods, which primarily focus on sparse DTI networks and have poor performance in aggregating information from neighboring nodes and representing isolated nodes within the network. In this study, we propose a novel deep learning framework, named GIAE-DTI, which considers cross-modal similarity of drugs and targets and constructs a heterogeneous network for DTI prediction. Firstly, the model calculates the cross-modal similarity of drugs and proteins from the relationships among drugs, proteins, diseases, and side effects, and performs similarity integration by taking the average. Then, a drug-target heterogeneous network is constructed, including drug-drug interactions, protein-protein interactions, and drug-target interactions processed by weighted K nearest known neighbors. In the heterogeneous network, a graph autoencoder based on a graph isomorphism network is employed for feature extraction, while a dual decoder is utilized to achieve better self-supervised learning, resulting in latent feature representations for drugs and targets. Finally, a deep neural network is employed to predict DTIs. The experimental results indicate that on the benchmark dataset, GIAE-DTI achieves AUC and AUPR scores of 0.9533 and 0.9619, respectively, in DTI prediction, outperforming the current state-of-the-art methods. Additionally, case studies on four 5-hydroxytryptamine receptor-related targets and five drugs related to mental diseases show the great potential of the proposed method in practical applications.
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ISSN:2168-2194
2168-2208
2168-2208
DOI:10.1109/JBHI.2024.3458794