Suppr超能文献

疾病与化合物筛选的网络视图。

A network view of disease and compound screening.

作者信息

Schadt Eric E, Friend Stephen H, Shaywitz David A

机构信息

Eric E. Schadt is at Rosetta Inpharmatics, LLC, Merck & Co., Inc., 401 Terry Avenue North, Seattle, Washington 98109, USA.

出版信息

Nat Rev Drug Discov. 2009 Apr;8(4):286-95. doi: 10.1038/nrd2826.

Abstract

The large-scale generation and integration of genomic, proteomic, signalling and metabolomic data are increasingly allowing the construction of complex networks that provide a new framework for understanding the molecular basis of physiological or pathophysiological states. Network-based drug discovery aims to harness this knowledge to investigate and understand the impact of interventions, such as candidate drugs, on the molecular networks that define these states. In this article, we describe how such an approach offers a novel way to understand biology, characterize disease and ultimately develop improved therapies, and discuss the challenges to realizing these goals.

摘要

基因组学、蛋白质组学、信号转导组学和代谢组学数据的大规模生成与整合,越来越有助于构建复杂网络,为理解生理或病理生理状态的分子基础提供了一个新框架。基于网络的药物发现旨在利用这些知识,来研究和理解诸如候选药物等干预措施对界定这些状态的分子网络的影响。在本文中,我们描述了这种方法如何提供一种理解生物学、表征疾病并最终开发出改良疗法的新途径,并讨论了实现这些目标所面临的挑战。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验