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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.<\/jats:p>","DOI":"10.1186\/s12859-026-06420-4","type":"journal-article","created":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T12:47:30Z","timestamp":1773233250000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["C2M-Mamba: drug-drug interaction prediction based on cross-modal cross-Mamba"],"prefix":"10.1186","volume":"27","author":[{"given":"Shanwen","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuanlei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dengwu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,11]]},"reference":[{"issue":"1","key":"6420_CR1","doi-asserted-by":"publisher","first-page":"24616","DOI":"10.1038\/s41598-025-10240-6","volume":"15","author":"A Zhang","year":"2025","unstructured":"Zhang A, Sun X, Wu Q, et al. 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