MMB-DTI: A Multimodal State Space Framework with Bidirectional Cross-Attention for Drug-Target Interaction Prediction

Accurate drug-target interaction (DTI) prediction is fundamental to computational drug discovery. However, existing deep learning methods typically oversimplify biochemical entities as 1D sequences, neglecting crucial spatial topologies, and incur prohibitive quadratic computational costs when modeling long-range protein dependencies. To overcome these bottlenecks, we propose MMB-DTI, a novel multimodal framework that synergistically fuses evolutionary and chemical semantics, extracted via pre-trained language models (ESM-2 and ChemBERTa), with structural topologies derived from graph neural networks. At its core, the framework introduces a hybrid Mamba-BiLSTM architecture, leveraging a selective state space model for linear-time encoding of ultra-long-range protein sequences while precisely preserving local sequential motifs. Furthermore, a bidirectional cross-attention module simulates the biological "induced-fit" mechanism, dynamically capturing reciprocal and fine-grained binding affinities. Comprehensive evaluations across six benchmark datasets (Davis, KIBA, DrugBank, Enzymes, Ion Channels, and GPCRs) demonstrate that MMB-DTI achieves highly competitive predictive performance, significantly outperforming state-of-the-art baselines. Our results highlight that coupling linear-time state space modeling with topological complementarity yields a robust and discriminative paradigm for deciphering complex drug-target associations.