A Node-Adaptive Feature Fusion Network for Drug–Target Interaction Prediction Based on Multi-View Graphs

Existing drug–target interaction (DTI) prediction methods still face challenges caused by sparse interaction data, complex multi-source relationships, and imbalanced information contributions among different nodes. In this study, we propose NAFF-DTI, a node-level adaptive feature fusion network based on multi-view graphs. The model uniformly represents drug similarity, target similarity, and known drug–target interactions as multiple relational views, and learns node representations through graph encoding and cross-view representation learning. To more effectively utilize heterogeneous relational information, NAFF-DTI introduces cross-view feature discrepancy modeling and a node-level adaptive fusion mechanism to dynamically adjust the contribution of different views according to node structural characteristics. Experimental results show that NAFF-DTI achieves the best AUC and AUPR on all five benchmark datasets. Compared with the strongest baseline for each dataset and metric, NAFF-DTI achieves average relative improvements of 3.81% in AUC and 3.23% in AUPR. It can also improve the utilization of multi-source information, maintain relatively stable prediction under different data distributions, and prioritize biologically plausible candidate drug–target associations from the unannotated candidate space. These results indicate that NAFF-DTI can provide computational support for DTI candidate prioritization and repurposing-oriented hypothesis generation.