Drug-Target Interaction Prediction via Dual-Interaction Fusion.

Accurate prediction of drug-target interaction (DTI) is crucial for modern drug discovery. However, experimental assays are costly, and many existing computational models still face challenges in capturing multi-scale features, fusing cross-modal information, and modeling fine-grained drug-protein interactions. To address these challenges, We propose Gated-Attention Dual-Fusion Drug-Target Interaction (GADFDTI), whose core contribution is a fusion module that constructs an explicit atom-residue similarity field, refines it with a lightweight 2D neighborhood operator, and performs gated bidirectional aggregation to obtain interaction-aware representations. To provide strong and width-aligned unimodal inputs to this fusion module, we integrate a compact multi-scale dense GCN for drug graphs and a masked multi-scale self-attention protein encoder augmented by a narrow 1D-CNN branch for local motif aggregation. Experiments on two benchmarks, Human and C. elegans, show that GADFDTI consistently outperforms several recently proposed DTI models, achieving AUC values of 0.986 and 0.996, respectively, with corresponding gains in precision and recall. A SARS-CoV-2 case study further demonstrates that GADFDTI can reliably prioritize clinically supported antiviral agents while suppressing inactive compounds, indicating its potential as an efficient in silico prescreening tool for lead-target discovery.