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The different strategies for designing GPCR and kinase targeted libraries
J F Lowrie1, R K Delisle, D W Hobbs
1Molecular Modeling, Pharmacopeia, Inc., CN5350, Princeton, New Jersey 08543-5350, USA. ddiller@pharmacop.com
Combinatorial Chemistry & High Throughput Screening
|August 24, 2004
Summary
Computational tools for target class library design are reviewed, focusing on protein kinases and G-protein coupled receptors (GPCRs). Useful tools extract trends from data like docking and clustering for drug discovery.
Area of Science:
- Computational chemistry
- Medicinal chemistry
- Drug discovery
Background:
- Combinatorial library design increasingly incorporates target class focusing alongside diversity and drug-likeness.
- Computational tools are essential for navigating the complexities of modern drug discovery libraries.
Purpose of the Study:
- To review available computational tools for target class library design.
- To highlight their utility in structure-based and ligand-based approaches.
- To identify areas for improvement in computational tool development.
Main Methods:
- Review of computational tools for target class focused library design.
- Illustration using protein kinase (structure-based) and GPCR (ligand-based) families.
- Focus on tools extracting trends from computational data (docking, clustering, data mining).
Main Results:
- Tools that extract trends from computational data, such as docking, clustering, and data mining of structure-activity relationships (SAR) or high-throughput screening (HTS) data, are most effective.
- Structure-based design is exemplified by protein kinases; ligand-based design is illustrated by G-protein coupled receptors (GPCRs).
Conclusions:
- Current tools are often applied "brute-force" to large target families.
- Improvements are needed in tools for pattern extraction across families, efficient model application, mining of ADMET and targeting data, and interactive virtual space exploration.