PDF(Pubmed)
Abstract：
Drug-drug interactions play a vital role in drug research. However, they may also cause adverse reactions in patients, with serious consequences. Manual detection of drug-drug interactions is time-consuming and expensive, so it is urgent to use computer methods to solve the problem. There are two ways for computers to identify drug interactions: one is to identify known drug interactions, and the other is to predict unknown drug interactions. In this paper, we review the research progress of machine learning in predicting unknown drug interactions. Among these methods, the literature-based method is special because it combines the extraction method of DDI and the prediction method of DDI. We first introduce the common databases, then briefly describe each method, and summarize the advantages and disadvantages of some prediction models. Finally, we discuss the challenges and prospects of machine learning methods in predicting drug interactions. This review aims to provide useful guidance for interested researchers to further promote bioinformatics algorithms to predict DDI.
摘要：
药物相互作用在药物研究中起着至关重要的作用。然而,它们也可能导致患者的不良反应，有严重的后果。手动检测药物-药物相互作用耗时且昂贵，所以迫切需要用计算机的方法来解决这个问题。计算机识别药物相互作用有两种方法:一种是识别已知的药物相互作用,另一个是预测未知的药物相互作用。在本文中,本文综述了机器学习预测未知药物相互作用的研究进展。在这些方法中,基于文献的方法是特殊的，因为它结合了DDI的提取方法和DDI的预测方法。我们首先介绍常见的数据库，然后简要描述每种方法，并总结了一些预测模型的优缺点。最后,我们讨论了机器学习方法在预测药物相互作用方面的挑战和前景。这篇综述旨在为感兴趣的研究人员进一步推广生物信息学算法预测DDI提供有用的指导。
