ArticleiScience2026
Unveiling rare drug interactions via a BioGPT-enhanced dual graph framework for robust pharmacovigilance.
Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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7 authors.
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Abstract
Uncovering rare drug-drug interactions (DDIs) is pivotal for patient safety, yet hampered by severe class imbalance and semantic complexity in biomedical text. To address this, we present BIDGNN, a framework synergizing generative augmentation with dual-graph reasoning. We employ a BioGPT-driven generative adversarial network (BioGPT-GAN) to synthesize realistic training samples for underrepresented interactions, resolving data scarcity. Complementarily, a dual-graph architecture captures both explicit syntactic dependencies and implicit semantic correlations, fused via cross-modal attention. BIDGNN achieves an F1-score of 83.57% on the DDI 2013 benchmark, demonstrating superior sensitivity to minority interaction types. This work offers a robust, interpretable solution for automated pharmacovigilance, effectively translating massive literature into clinical safety insights.
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