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 contexts; and rigid prediction heads discarding spatial interaction patterns. These issues hamper the accuracy and comprehensiveness 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 Networks (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 framework maintains consistent performance across datasets of varying scales, and case studies validate its practical utility in real-world drug development pipelines.