Details

Autor(en) / Beteiligte

• Sciabola, Simone
• Stanton, Robert V
• Wittkopp, Sarah
• Wildman, Scott
• Moshinsky, Deborah
• Potluri, Shobha
• Xi, Hualin

Titel

Predicting Kinase Selectivity Profiles Using Free-Wilson QSAR Analysis

Ist Teil von

• Journal of Chemical Information and Modeling, 2008-09, Vol.48 (9), p.1851-1867

Ort / Verlag

United States: American Chemical Society

Erscheinungsjahr

2008

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• Kinases are involved in a variety of diseases such as cancer, diabetes, and arthritis. In recent years, many kinase small molecule inhibitors have been developed as potential disease treatments. Despite the recent advances, selectivity remains one of the most challenging aspects in kinase inhibitor design. To interrogate kinase selectivity, a panel of 45 kinase assays has been developed in-house at Pfizer. Here we present an application of in silico quantitative structure activity relationship (QSAR) models to extract rules from this experimental screening data and make reliable selectivity profile predictions for all compounds enumerated from virtual libraries. We also propose the construction of R-group selectivity profiles by deriving their activity contribution against each kinase using QSAR models. Such selectivity profiles can be used to provide better understanding of subtle structure selectivity relationships during kinase inhibitor design.