TP-DDI: Transformer-based pipeline for the extraction of Drug-Drug Interactions

Drug-Drug Interaction (DDI) extraction is the task of identifying drug entities and the potential interactions between drug pairs from biomedical literature. Computer-aided extraction of DDIs is vital for drug discovery, as this process remains extremely expensive and time consuming. Therefore, Machine Learning-based approaches can reduce the laborious task during the drug development cycle. Numerous traditional and Neural Network based approaches for Drug Named Entity Recognition (DNER) and the classification of DDIs have been proposed over the years. However, despite the development of many effective methods, achieving good prediction accuracy is an area where significant improvement can be made. In this article, we present a novel end-to-end approach that tackles the overall DDI extraction task as a pipelined method via the Transformer model architecture and biomedical domain pre-trained weights. In our approach, the tasks of DNER and DDI classification are executed successively to extract the drug entities and to classify their relationship respectively. The proposed approach, TP-DDI, integrates prior knowledge by using pre-trained weights from BioBERT and improves in both the Drug Named Entity Recognition and the overall DDI extraction task over the current state-of-the-art approaches on the DDI Extraction 2013 corpus.

Automated summary

Drug-Drug Interaction (DDI) extraction involves identifying drug entities and potential interactions between drug pairs in biomedical literature. Machine Learning approaches are essential for reducing the laborious task of drug discovery, but there is still room for improvement in terms of prediction accuracy.