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预测药物不良反应的网络医学和系统药理学方法。

Network medicine and systems pharmacology approaches to predicting adverse drug effects.

作者信息

Funari Alessio, Fiscon Giulia, Paci Paola

机构信息

Department of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy.

Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy.

出版信息

Br J Pharmacol. 2024 Sep 11. doi: 10.1111/bph.17330.

Abstract

Identifying and understanding the relationships between drug intake and adverse effects that can occur due to inadvertent molecular interactions between drugs and targets is a difficult task, especially considering the numerous variables that can influence the onset of such events. The ability to predict these side effects in advance would help physicians develop strategies to avoid or counteract them. In this article, we review the main computational methods for predicting side effects caused by drug molecules, highlighting their performance, limitations and application cases. Furthermore, we provide an overall view of resources, such as databases and tools, useful for building side effect prediction analyses.

摘要

识别并理解药物摄入与因药物和靶点之间无意的分子相互作用而可能产生的不良反应之间的关系是一项艰巨的任务,尤其是考虑到可能影响此类事件发生的众多变量。提前预测这些副作用的能力将有助于医生制定避免或对抗它们的策略。在本文中,我们回顾了预测药物分子引起的副作用的主要计算方法,突出了它们的性能、局限性和应用案例。此外,我们还提供了对资源(如数据库和工具)的总体概述,这些资源有助于构建副作用预测分析。

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