Deep learning for drug–drug interaction extraction from the literature: a review

Abstract

Abstract Drug–drug interactions (DDIs) are crucial for drug research and pharmacovigilance. These interactions may cause adverse drug effects that threaten public health and patient safety. Therefore, the DDIs extraction from biomedical literature has been widely studied and emphasized in modern biomedical research. The previous rules-based and machine learning approaches rely on tedious feature engineering, which is labourious, time-consuming and unsatisfactory. With the development of deep learning technologies, this problem is alleviated by learning feature representations automatically. Here, we review the recent deep learning methods that have been applied to the extraction of DDIs from biomedical literature. We describe each method briefly and compare its performance in the DDI corpus systematically. Next, we summarize the advantages and disadvantages of these deep learning models for this task. Furthermore, we discuss some challenges and future perspectives of DDI extraction via deep learning methods. This review aims to serve as a useful guide for interested researchers to further advance bioinformatics algorithms for DDIs extraction from the literature.