Chen Yun-Gu, Wang Yin-Ying, Zhao Xing-Ming
Department of Computer Science and Technology, Tongji University, Shanghai 201804, China..
Curr Top Med Chem. 2016;16(30):3629-3635. doi: 10.2174/1568026616666160530182013.
Adverse drug reactions (ADRs) are the leading factors of drug attrition in drug development and post-market drug withdrawal. The identification of potential ADRs can help prevent the failure of drug discovery and improve development efficiency. Furthermore, elucidating possible ADRs for known drugs can help better understand the mechanism of drug actions and even find new indications of old drugs. Unfortunately, only the ADRs of some well-studied drugs are available and our knowledge about ADRs of available drugs is far from complete. Recently, with more structural and omics data available, some computational approaches have been developed for predicting drug ADRs. In this review, we present a survey on the recent progresses on computational methodologies that have been developed for ADR prediction based on various kinds of data, and some ADR related resources are also introduced.
药物不良反应（ADR）是药物研发中药物淘汰以及上市后药物撤市的主要因素。识别潜在的药物不良反应有助于预防药物研发失败并提高研发效率。此外，阐明已知药物可能的不良反应有助于更好地理解药物作用机制，甚至发现老药的新适应症。遗憾的是，仅有一些研究充分的药物的不良反应信息可得，而且我们对现有药物不良反应的了解还远不完整。近年来，随着更多结构和组学数据的出现，已开发出一些用于预测药物不良反应的计算方法。在本综述中，我们对基于各类数据开发的用于药物不良反应预测的计算方法的最新进展进行了综述，并介绍了一些与药物不良反应相关的资源。
