MAGC-DTI: modality-shared space and adaptive gated interactive cross-attention for drug-target interaction prediction

Accurate drug-target interaction (DTI) prediction is crucial for drug repurposing and accelerating drug development. Although deep learning has advanced DTI prediction, existing methods struggle with two key challenges: (i) capturing complex hierarchical patterns in protein sequences, and (ii) enabling effective bidirectional information exchange between drug and protein modalities. We propose MAGC-DTI, an end-to-end cross-modal framework that integrates bidirectional information exchange into both feature extraction and fusion stages through three key innovations: (i) multi-scale attention aggregation (MSAA) for hierarchical protein pattern capture, (ii) adaptive gated interactive cross attention (AGICA) for context-aware cross-modal interaction, and (iii) multi-path residual classifier (MPRC) for modality-preserving fusion. Comprehensive evaluations on six benchmark datasets show that MAGC-DTI generally achieves favorable performance relative to seven state-of-the-art baselines, with competitive results in cold-start and cross-domain scenarios. The model also provides interpretable insights through attention visualization and case studies confirm the biological relevance of learned representations.