Deep learning for drug-drug interaction prediction: A comprehensive review

Abstract

The prediction of drug-drug interactions (DDIs) is a crucial task for drug safety research, and identifying potential DDIs helps us to explore the mechanism behind combinatorial therapy. Traditional wet chemical experiments for DDI are cumbersome and time-consuming, and are too small in scale, limiting the efficiency of DDI predictions. Therefore, it is particularly crucial to develop improved computational methods for detecting drug interactions. With the development of deep learning, several computational models based on deep learning have been proposed for DDI prediction. In this review, we summarized the high-quality DDI prediction methods based on deep learning in recent years, and divided them into four categories: neural network-based methods, graph neural network-based methods, knowledge graph-based methods, and multimodal-based methods. Furthermore, we discuss the challenges of existing methods and future potential perspectives. This review reveals that deep learning can significantly improve DDI prediction performance compared to traditional machine learning. Deep learning models can scale to large-scale datasets and accept multiple data types as input, thus making DDI predictions more efficient and accurate.

Keywords

deep learning / drug-drug interactions / graph neural network / knowledge graph / multimodal deep learning / neural network

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