Accurately predicting potential drug-drug interactions (DDIs) from multimodal data is critical for medication safety and adverse drug reaction prevention. Existing methods face challenges in modeling long-range dependencies and effectively integrating heterogeneous features from structured molecular data and unstructured text. To address these limitations, we propose C2M-Mamba, a cross-modal framework that integrates convolutional neural networks, Mamba, and cross-Mamba (CroMamba) to capture discriminative features from drug descriptions, SMILES sequences, and social media texts. The model efficiently handles long-range dependencies through state space models while enabling effective cross-modal fusion. Comprehensive evaluations on the DDIExtraction2013 dataset demonstrate that C2M-Mamba outperforms 10 state-of-the-art baselines, achieving 82.37% precision, 80.98% F1-score, and 88.73% AUC. The proposed approach also exhibits robust performance in handling class imbalance and provides interpretable predictions, offering a reliable solution for multimodal DDI prediction with potential applications in pharmacovigilance and personalized medicine.
Zhang et al. (Wed,) studied this question.