Abstract: Drug-target interaction(DTI) prediction is a core component of drug discovery and repurposing.Existing models face significant challenges in multiscale modeling of target sequences and multimodal feature fusion:traditional methods based on local convolutions lose global dependencies,while Transformers suffer from quadratic complexity for long sequences,and heterogeneous feature fusion often triggers semantic conflicts and overfitting.To address these issues,this paper proposes G2MambaDTI,a novel framework based on a gated bidirectional Mamba network for multimodal feature collaborative modeling.The method employs a cascaded CNN and Transformer encoder architecture to process target sequences,utilizing an adaptive gating mechanism to balance local functional motifs and global dependency features.Furthermore,a cross-modal feature calibration module with adaptive gates is introduced to enhance critical interaction pattern representations.Finally,a bidirectional Mamba architecture is integrated to capture cross-modal long-range interactions between drug molecular graphs and target sequences,leveraging its selective state-space modeling and linear complexity to significantly improve efficiency for long sequences.Experimental comparisons on four public datasets against five other deep learning models demonstrate that the proposed method outperforms existing approaches across all performance metrics,validating its superiority in DTI prediction.

Key words: Drug-target interactions, Multimodal, Deep learning, Transformer, Mamba