Optimized Drug-Drug Interaction Extraction With BioGPT and Focal Loss-Based Attention.

Abstract

Drug-drug interactions (DDIs) are a significant focus in biomedical research and clinical practice due to their potential to compromise treatment outcomes or cause adverse effects. While deep learning approaches have advanced DDI extraction, challenges such as severe class imbalance and the complexity of biomedical relationships persist. This study introduces BioFocal-DDI, a framework combining BioGPT for data augmentation, BioBERT and BiLSTM for contextual and sequential feature extraction, and Relational Graph Convolutional Networks (ReGCN) for relational modeling. To address class imbalance, a Focal Loss-based Attention mechanism is employed to enhance learning on underrepresented and challenging instances. Evaluated on the DDI Extraction 2013 dataset, BioFocal-DDI achieves a precision of 86.75%, recall of 86.53%, and an F1 Score of 86.64%. These results suggest that the proposed method is effective in improving DDI extraction. All our code and data have been publicly released at https://github.com/Hero-Legend/BioFocal-DDI.