IEEE/ACM Trans Comput Biol Bioinform. 2022 Jul-Aug;19(4):1968-1985. doi: 10.1109/TCBB.2021.3081268. Epub 2022 Aug 8.
The detection of drug-drug interactions (DDIs) is a crucial task for drug safety surveillance, which provides effective and safe co-prescriptions of multiple drugs. Since laboratory researches are often complicated, costly and time-consuming, it's urgent to develop computational approaches to detect drug-drug interactions. In this paper, we conduct a comprehensive review of state-of-the-art computational methods falling into three categories: literature-based extraction methods, machine learning-based prediction methods and pharmacovigilance-based data mining methods. Literature-based extraction methods detect DDIs from published literature using natural language processing techniques; machine learning-based prediction methods build prediction models based on the known DDIs in databases and predict novel ones; pharmacovigilance-based data mining methods usually apply statistical techniques on various electronic data to detect drug-drug interaction signals. We first present the taxonomy of drug-drug interaction detection methods and provide the outlines of three categories of methods. Afterwards, we respectively introduce research backgrounds and data sources of three categories, and illustrate their representative approaches as well as evaluation metrics. Finally, we discuss the current challenges of existing methods and highlight potential opportunities for future directions.
药物-药物相互作用（DDI）的检测是药物安全监测的一项重要任务，它为多种药物的有效和安全联合使用提供了依据。由于实验室研究通常复杂、昂贵且耗时，因此迫切需要开发计算方法来检测药物-药物相互作用。本文对三种类型的最新计算方法进行了全面综述：基于文献的提取方法、基于机器学习的预测方法和基于药物警戒的数据挖掘方法。基于文献的提取方法使用自然语言处理技术从已发表的文献中检测药物-药物相互作用；基于机器学习的预测方法基于数据库中已知的药物-药物相互作用构建预测模型，并预测新的药物-药物相互作用；基于药物警戒的数据挖掘方法通常在各种电子数据上应用统计技术来检测药物-药物相互作用信号。我们首先介绍了药物-药物相互作用检测方法的分类，并提供了这三种方法的概述。然后，我们分别介绍了这三种方法的研究背景和数据来源，并举例说明了它们的代表性方法和评估指标。最后，我们讨论了现有方法面临的挑战，并强调了未来发展的潜在机遇。
