Global mapping of pharmacological space

被引：589

作者：

Paolini, Gaia V.

Shapland, Richard H. B.

van Hoorn, Willem P.

Mason, Jonathan S.

Hopkins, Andrew L. ^{[1
]}

机构：

[1] Pfizer Global Res & Dev, Dept Knowledge Discovery, Sandwich CT13 9NJ, Kent, England

[2] Pfizer Global Res & Dev, Dept Computat Chem, Sandwich CT13 9NJ, Kent, England

[3] Pfizer Global Res & Dev, Dept Med Informat Struct & Design, Sandwich CT13 9NJ, Kent, England

[4] Pfizer Global Res & Dev, Dept Res Informat, Sandwich CT13 9NJ, Kent, England

[5] Servefile Software Ltd, Bristol BS48 4SG, North Somerset, England

[6] Lundbeck Res, DK-2500 Copenhagen, Denmark

来源：

NATURE BIOTECHNOLOGY | 2006年 / 24卷 / 07期

关键词：

D O I：

10.1038/nbt1228

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 ; 0836 ; 090102 ; 100705 ;

摘要：

We present the global mapping of pharmacological space by the integration of several vast sources of medicinal chemistry structure-activity relationships (SAR) data. Our comprehensive mapping of pharmacological space enables us to identify confidently the human targets for which chemical tools and drugs have been discovered to date. The integration of SAR data from diverse sources by unique canonical chemical structure, protein sequence and disease indication enables the construction of a ligand-target matrix to explore the global relationships between chemical structure and biological targets. Using the data matrix, we are able to catalog the links between proteins in chemical space as a polypharmacology interaction network. We demonstrate that probabilistic models can be used to predict pharmacology from a large knowledge base. The relationships between proteins, chemical structures and drug-like properties provide a framework for developing a probabilistic approach to drug discovery that can be exploited to increase research productivity.

引用

页码：805 / 815

页数：11