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Global mapping of pharmacological space
Gaia V Paolini1, Richard H B Shapland, Willem P van Hoorn
1The Department of Knowledge Discovery, Pfizer Global Research and Development, Sandwich, Kent CT13 9NJ, UK.
Nature Biotechnology
|July 15, 2006
Summary
This study maps global pharmacological space by integrating medicinal chemistry data, identifying human targets for discovered drugs and chemical tools. This framework aids in developing probabilistic approaches for more productive drug discovery.
Area of Science:
- Medicinal Chemistry
- Pharmacology
- Computational Biology
Background:
- Medicinal chemistry generates vast structure-activity relationship (SAR) data.
- Understanding the mapping of chemical structures to biological targets is crucial for drug discovery.
- Existing data sources are often siloed, limiting comprehensive analysis.
Purpose of the Study:
- To create a global map of pharmacological space by integrating diverse SAR data.
- To identify human targets for which chemical tools and drugs have been discovered.
- To establish a framework for a probabilistic approach to drug discovery.
Main Methods:
- Integration of SAR data from multiple sources using canonical chemical structure, protein sequence, and disease indication.
- Construction of a ligand-target matrix to explore chemical structure-biological target relationships.
- Cataloging protein interactions in chemical space to form a polypharmacology network.
Main Results:
- Successful global mapping of pharmacological space.
- Identification of human targets linked to discovered chemical tools and drugs.
- Demonstration of probabilistic models for predicting pharmacology from large knowledge bases.
- Creation of a network representing polypharmacology interactions.
Conclusions:
- The integrated data matrix provides a comprehensive view of chemical space and target relationships.
- Probabilistic models can effectively predict pharmacological activity.
- This framework supports a probabilistic approach to enhance drug discovery productivity.
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