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

ABSTRACT: Reliable prediction of drug-target interaction (DTI) is essential for accelerating drug discovery, yet remains hindered by data imbalance, limited interpretability, and neglect of protein dynamics. Here, we present GHCDTI, a heterogeneous graph neural framework designed to overcome these challenges through three synergistic innovations. First, cross-view contrastive learning with adaptive positive sampling improves generalization under extreme class imbalance (positive/negative ratio<1:100). Second, heterogeneous data fusion integrates molecular graphs, protein structure graphs, and bioactivity data via cross-graph attention, enabling interpretable residue-level insights. Third, multi-scale wavelet feature extraction captures both conserved and dynamic structural features by decomposing pr

SUBMITTER: Dai W 

PROVIDER: S-EPMC12365291 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature