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<p>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.</p>

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C2M-Mamba: drug-drug interaction prediction based on cross-modal cross-Mamba

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月11日

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  • Shanwen Zhang,
  • Chuanlei Zhang,
  • Dengwu Wang

摘要

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.