CAMA-DTI: a Cross-Domain Attention Empowered Mamba Architecture for Interpretable DTI Prediction.

Drug-target interaction (DTI) prediction plays a pivotal role in accelerating drug discovery. Nevertheless, existing AI-driven approaches face three critical limitations: existing attention-based methods lack dynamic bidirectional interaction channels, limiting their ability to model asymmetric drug-target communication patterns; conventional architectures struggle to integrate both local binding patterns and global biological con texts; and rigid prediction heads discarding spatial interaction patterns. These issues hamper the accuracy and comprehensive ness of DTI prediction. To address these, we propose CAMA DTI, an end-to-end framework integrating three innovations. First, a cross-domain bidirectional attention module establishes dual-perspective interaction channels that enable co-evolutionary feature refinement through mutual pharmacological feedback. Second, the Mamba-driven fusion block incorporates state-space modeling to dynamically integrate local binding patterns with global biological contexts across extended sequences. Third, we replace conventional classifiers with Kolmogorov-Arnold Net works (KANs) employing adaptive spline transformations that preserve multi-scale interaction signatures while maintaining parametric efficiency. Extensive experimental results demonstrate that CAMA-DTI achieves robust and accurate predictions across diverse datasets, outperforming state-of-the-art methods in both established and novel drug target scenarios. Notably, the frame work maintains consistent performance across datasets of varying scales, and case studies validate its practical utility in real-world drug development pipelines.