Selectivity has always been important in drug discovery. However, in the age of genetics it has an enhanced focus for the discovery of efficacious small molecule therapeutics. In particular it has become increasingly important to engineer selectivity for one member of a close family of biological targets. To do this effectively medicinal chemists must find, understand and exploit features of their series that impart the desired selectivity.Activity cliffs are an established and useful method for rapidly finding critical regions in the structure activity (SAR) landscape. These are regions of a molecule where small changes in the structure cause significantly larger changes in the activity of the series. We recently presented a method for the identification and understanding of activity cliffs using the shape and especially the electrostatic character of the molecules.In this poster we present an extension to our previous method where we rapidly identify molecule features that are critical to the improvement of the selectivity of a lead series. Critical to this approach is clear visualisation of large datasets that can contain many activity measurements. Once identified, the reason for the selectivity change must be thoroughly understood in order to improve on the observed selectivity and progress the series through to candidate. To do this we present the use of 3D shape and electrostatic difference maps that clearly and concisely communicate the important changes in the binding characteristics of the molecules involved in the selectivity cliff.

Rapid identification and understanding of selectivity cliffs

248th National Meeting (2014)

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