Heterogeneous network drug-target interaction prediction model based on graph wavelet transform and multi-level contrastive learning

2025/04/27 by Dai, Wenfeng, Yanhong Wang, Shuai Yan +6
Biochemistry, Genetics and Molecular Biology · Computer Science · #Advanced Graph Neural Networks #Artificial Intelligence (cs.AI) #Computational Drug Discovery Methods #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Bioinformatics #Quantitative Methods (q-bio.QM)

paper · pdf · doi:10.48550/arxiv.2504.20103

openalex publication_date 2025/04/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Drug-target interaction (DTI) prediction is a core task in drug development and precision medicine in the biomedical field. However, traditional machine learning methods generally have the black box problem, which makes it difficult to reveal the deep correlation between the model decision mechanism and the interaction pattern between biological molecules. This study proposes a heterogeneous network drug target interaction prediction framework, integrating graph neural network and multi scale signal processing technology to construct a model with both efficient prediction and multi level interpretability. Its technical breakthroughs are mainly reflected in the following three dimensions:Local global feature collaborative perception module. Based on heterogeneous graph convolutional neural network (HGCN), a multi order neighbor aggregation strategy is designed.Multi scale graph signal decomposition and biological interpretation module. A deep hierarchical node feature transform (GWT) architecture is proposed.Contrastive learning combining multi dimensional perspectives and hierarchical representations. By comparing the learning models, the node representations from the two perspectives of HGCN and GWT are aligned and fused, so that the model can integrate multi dimensional information and improve the prediction robustness. Experimental results show that our framework shows excellent prediction performance on all datasets. This study provides a complete solution for drug target discovery from black box prediction to mechanism decoding, and its methodology has important reference value for modeling complex biomolecular interaction systems.

Citations

• A Cross-Field Fusion Strategy for Drug-Target Interaction Prediction
• Drug target prediction through deep learning functional representation of gene signatures
• AttentionMGT-DTA: A multi-modal drug-target affinity prediction using graph transformer and attention mechanism
• Gated Attention Coding for Training High-performance and Efficient Spiking Neural Networks
• Multi-Head Attention: Collaborate Instead of Concatenate
• DeepGCNs: Can GCNs Go as Deep as CNNs?
• node2vec: Scalable Feature Learning for Networks
• node2vec

Related

• A Heterogeneous Network-based Contrastive Learning Approach for Predicting Drug-Target Interaction
• Predicting Drug-Drug Interactions Using Heterogeneous Graph Neural Networks: HGNN-DDI
• Heterogeneous networks in drug-target interaction prediction
• A Graph-in-Graph Learning Framework for Drug-Target Interaction Prediction
• Drug-Target Interaction Prediction with Graph Attention networks