基于多视图对比与同源特征的药物靶标亲和力预测研究

doi:10.11896/jsjkx.250300168

Abstract

Abstract: Existing graph neural network(GNN)-based methods for drug-target affinity(DTA) prediction exhibit notable limitations in feature utilization and view alignment.Specifically,1) they fail to sufficiently model the feature representations of drugs and proteins within their respective views,leading to incomplete intra-view feature learning and restricted exploitation of feature information;2) they are unable to effectively align the intrinsic correlations across different views,thereby limiting cross-view information synergy.To address these challenges,a MVHGNN(Multi-View Hybrid Homogeneous Graph Neural Network ) is proposed.MVHGNN constructs a multi-view contrastive learning framework,employing an ESTGCN(Enhanced Subgraph Topology Graph Convolutional Network) and a GIN(Graph Isomorphism Network) as encoders in the drug molecular view and protein view,respectively,to capture the topological and hierarchical features of drugs and proteins.Furthermore,homology information is integrated to enhance intra-view feature representation and utilization.In the drug-target affinity view,a GCN(Graph Convolutional Network) is used to extract global topological information,enabling the construction of drug-protein interaction representations.A cross-view contrastive learning strategy is further adopted to maximize mutual information between drugs and proteins across different views,enhancing representation consistency and cross-view collaboration.Experimental results demonstrate that MVHGNN achieves superior performance on two benchmark datasets,notably reaching a mean squared error(MSE)of 0.166 and a modified determination coefficient(r²_m) of 0.794 on the Davis dataset,outperforming existing state-of-the-art methods.

Key words: Multi-view contrastive learning, Topological features, Homology information, Drug-target affinity(DTA), Cross-view contrastive learning

CLC Number:

TP391

TIAN Xin, ZHU Guosheng, XIONG Yuran, WU You. Study on Drug Target Affinity Prediction Based on Multi-view Comparison and Homology Information[J].Computer Science, 2026, 53(5): 337-345.

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