Variations towards an efficient drug-drug interaction

Drug-drug interactions (DDIs) are a crucial research focus in clinical pharmacology and public health. DDIs can lead to reduced drug efficacy or increased adverse reactions, making the effective identification and understanding of drug interactions essential for patient safety and treatment outcomes. With the rapid growth of biomedical literature, automated methods for extracting DDI information have become increasingly necessary. In this paper, we propose BLRG, a novel model that uniquely integrates BioBERT, long short-term memory (LSTM), and relational graph convolutional network (R-GCN) to extract complex DDIs. This combination allows the model to effectively capture both semantic and relational features, outperforming existing methods in handling intricate dependencies in biomedical texts. Specifically, our approach begins by utilizing the BioBERT model to capture deep contextual features of sentences, extracting their semantic information. Following this, an LSTM network processes the sequential features of the sentence to model its contextual dependencies. Finally, an R-GCN is applied to identify and interpret the relationships between drug entities within the sentence, accurately capturing DDI information. Experimental results demonstrate that our model significantly outperforms current state-of-the-art methods across standard datasets, showcasing its effectiveness and potential in complex DDI extraction tasks. Our code and data are publicly available at: https://github.com/Hero-Legend/BLRG.