Affiliation:
1. School of Information and Control Engineering, China University of Mining and Technology , Xuzhou 221116 , China
2. School of Science, Jiangnan University , Wuxi 214122 , China
Abstract
Abstract
In clinical treatment, two or more drugs (i.e. drug combination) are simultaneously or successively used for therapy with the purpose of primarily enhancing the therapeutic efficacy or reducing drug side effects. However, inappropriate drug combination may not only fail to improve efficacy, but even lead to adverse reactions. Therefore, according to the basic principle of improving the efficacy and/or reducing adverse reactions, we should study drug–drug interactions (DDIs) comprehensively and thoroughly so as to reasonably use drug combination. In this review, we first introduced the basic conception and classification of DDIs. Further, some important publicly available databases and web servers about experimentally verified or predicted DDIs were briefly described. As an effective auxiliary tool, computational models for predicting DDIs can not only save the cost of biological experiments, but also provide relevant guidance for combination therapy to some extent. Therefore, we summarized three types of prediction models (including traditional machine learning-based models, deep learning-based models and score function-based models) proposed during recent years and discussed the advantages as well as limitations of them. Besides, we pointed out the problems that need to be solved in the future research of DDIs prediction and provided corresponding suggestions.
Funder
National Natural Science Foundation of China
Postgraduate Research & Practice Innovation Program of Jiangsu Province and the Postgraduate Research & Practice Innovation Program of China University of Mining and Technology
Publisher
Oxford University Press (OUP)
Subject
Molecular Biology,Information Systems
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