A Free-Wilson/Fujita-Ban analysis and prediction of the analgesic potency of some 3-hydroxy- and 3-methoxy-N-alkylmorphinan-6-one opioids

Application of Free-Wilson selectivity analysis for combinatorial library design

In this chapter we present an application of in silico quantitative structure-activity relationship (QSAR) models to establish a new ligand-based computational approach for generating virtual libraries. The Free-Wilson methodology was applied to extract rules from two data sets containing compounds which were screened against either kinase or PDE gene family panels. The rules were used to make predictions for all compounds enumerated from their respective virtual libraries. We also demonstrate the construction of R-group selectivity profiles by deriving activity contributions against each protein target using the QSAR models. Such selectivity profiles were used together with protein structural information from X-ray data to provide a better understanding of the subtle selectivity relationships between kinase and PDE family members.

Potent ligands for prokaryotic UDP-galactopyranose mutase that exploit an enzyme subsite

UDP-galactopyranose mutase (UGM or Glf), which catalyzes the interconversion of UDP-galactopyranose and UDP-galactofuranose, is implicated in the viability and virulence of multiple pathogenic microorganisms. Here we report the synthesis of high-affinity ligands for UGM homologues from Klebsiella pneumoniae and Mycobacterium tuberculosis. The potency of these compounds stems from their ability to access both the substrate binding pocket and an adjacent site.

Predicting kinase selectivity profiles using Free-Wilson QSAR analysis

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.