A meta-contrastive learning approach for clinical drug-drug interaction extraction from biomedical literature.

Drug-drug interactions (DDIs) are a significant source of adverse drug events and pose critical challenges to patient safety and clinical decision-making. Extracting DDIs from biomedical literature plays an essential role in pharmacovigilance, yet remains difficult due to data sparsity and high annotation costs. This study presents BioMCL-DDI, a novel few-shot learning framework that integrates meta-learning with contrastive embedding strategies to enable efficient DDI extraction under limited supervision. BioMCL-DDI jointly optimizes prototype-based classification and supervised contrastive representation learning within a unified architecture. The model captures both intra-class compactness and inter-class separability, enhancing its generalization in sparse biomedical settings. We evaluate BioMCL-DDI on three benchmark datasets: DDI-2013, DrugBank, and the more recent TAC 2018 DDI Extraction corpus. The model achieves F1 scores of 87.80% on DDI-2013, 86.00% on DrugBank, and 74.85%/74.82% on the two official test sets of TAC 2018, consistently outperforming competitive baselines. Our model significantly outperforms state-of-the-art baselines in low-resource scenarios. BioMCL-DDI provides a scalable and effective solution for DDI extraction from biomedical texts, with strong potential for integration into clinical decision support systems and biomedical knowledge bases. All our code and data have been publicly released at: https://github.com/Hero-Legend/BioMCL-DDI.