Nature Biotechnology 24, 805 - 815 (2006)
Published online: 13 July 2006; | doi:10.1038/nbt1228
Global mapping of pharmacological spaceGaia V Paolini1, 3, 7, Richard H B Shapland1, 4, 5, Willem P van Hoorn2, 3, Jonathan S Mason3, 6
& Andrew L Hopkins1, 3, 71
The Department of Knowledge Discovery, Pfizer Global Research and Development, Sandwich, Kent CT13 9NJ, UK. 2
The Department of Computational Chemistry, Pfizer Global Research and Development, Sandwich, Kent CT13 9NJ, UK. 3
The Department of Medicinal Informatics, Structure and Design, Pfizer Global Research and Development, Sandwich, Kent CT13 9NJ, UK. 4
The Department of Research Informatics, Pfizer Global Research and Development, Sandwich, Kent CT13 9NJ, UK. 5
Servefile Software Ltd., Nailsea, Bristol, North Somerset BS48 4SG, UK. 6
Lundbeck Research, Ottiliavej 9, DK-2500 Valby, Copenhagen, Denmark. 7
These authors contributed equally to this work.
Correspondence should be addressed to Andrew L Hopkins andrew.hopkins@pfizer.com 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.
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