Predicting drug-target interactions (DTI) is critical for drug discovery, influencing both the success and efficiency of drug development. Despite numerous methods exist, their predictive performance remains limited by irrelevant information and insufficient consideration of the spatial arrangement of proteins and drugs—both crucial for accurately modeling local interactions. To address these challenges, we propose FAPE-DTI, a deep learning model that integrates a focal attention network, a bilinear attention network, and relative positional encoding. The focal attention network leverages node importance scores to filter out irrelevant protein fragments, highlight key features, and reduce representational noise. The bilinear attention network further processes the refined protein and drug features to capture fine-grained pairwise interactions, constructing an atomic-level interaction graph that enhances the model’s structural awareness and interpretability. In addition, relative positional encoding—often overlooked in existing approaches—is incorporated into both the focal and bilinear attention modules to strengthen spatial interaction modeling. Experimental results show that FAPE-DTI consistently outperforms baseline models across multiple metrics on four benchmark datasets. Furthermore, case studies demonstrate that FAPE-DTI provides clearer insights into drug–target interaction regions at the atomic level, offering valuable interpretability to support rational drug design and development.