Bringing Kinases Into Focus: Efficient Drug Design Through the Use of Chemogenomic Toolkits

Abstract

The study of protein target families, as opposed to single targets, has become a very powerful tool in chemogenomics-led drug discovery. By integrating comprehensive chemoinformatics and bioinformatics databases with customised analytical tools, a 'Toolkit' approach for the target family is possible, thus allowing predictions of the ligand class, affinity, selectivity and likely off-target issues to be made for the guidance of the medicinal chemist. In this review, we highlight the development and application of the Toolkit approach to the protein kinase superfamily, drawing on examples from lead optimisation studies and the design of focused libraries for lead discovery.

How Large Does a Compound Screening Collection Need To Be?

“…Of course, it should be recognized that this high chemotype activity rate is in part a direct result of the design methodology utilized (sic. SoftFocus ® approach [24,28]) in their production and that such a hit rate could not necessarily be expected if screened against a target that was outside the design scope. Table 3 contains screening results for 9 in house assays for 15 chemotypes contained in SoftFocus ® GPCR targeted libraries.…”

“…The libraries considered in these studies incorporate a total of 85 molecular scaffolds, or cores, each of which has been designed to selectively target particular protein family classes, such as G-protein coupled receptors (GPCRs), protein kinases (PKs) and voltage-gated ion channels (VGICs) [23][24][25][26][27]. From the results, we will identify the theoretical minimum sizes for SoftFocus ® arrays that should still retain a useful SAR generation capability.…”

Measuring and interpreting the selectivity of protein kinase inhibitors

“…Several groups have looked at using a selection of these methods in order to predict which kinases will be inhibited by a compound. Some good correlations between calculated and experimental data were found as long as the compounds used for training were sufficiently structurally similar to those subsequently tested [21,26,66]. An analysis of the binding modes of many kinase inhibitors, as determined by X-ray crystallography, has suggested simple rules-of-thumb for predicting the orientation of typical ATP-competitive inhibitor scaffolds within the binding site [58].…”

In silico structure‐activity‐relationship (SAR) models from machine learning: a review

“…Frye's work advocated that such clusters will help in establishing correlations between sequence conservation and SAR homology, thus, making it possible to predict the cluster membership of a new protein based on its sequence. Since then, there have been a number of Chemogenomics efforts that have primarily focused on kinases [Vieth et al, 2004;Hu et al, 2005;Birault et al, 2006;Kellenberger et al, 2006;Hoppe et al, 2006], and GPCRs [Jacoby et al, 1999;Jacoby, 2001;Frimurer et al, 2005;Surgand et al, 2006]. Some of these approaches identify the right subset of family members using similarity search, either with respect to sequence [Frimurer et al, 2005;Surgand et al, 2006] or structure [Hu et al, 2005;Kellenberger et al, 2006;Hoppe et al, 2006], whereas other approaches employ machine-learning techniques to estimate and analyze the ligand-target affinity within each family Gough, 2002, 2005;Vieth et al, 2004;Jacob and Vert, 2008].…”