During lead optimisation the stepwise progression of compound activity is often disrupted by compounds that cause a disproportionately large (positive or negative) change in the biological response. These activity cliffs have long been recognised as an important source of information about the requirements of the protein for the series of interest. Activity cliff analysis has traditionally been done in 2D, but we have developed methods for expanding the dimensionality of activity cliff detection to include the 3D shape and electrostatic character of the ligands. In contrast to fingerprint similarity methods, accurate 3D similarity methods treat bioisosteres correctly which allows the identification of cliffs which the 2D methods fail to find.The detection of activity cliffs for the primary activity end point is a valuable addition to the arsenal of drug discovery scientists. However, modern drug discovery rarely proceeds through the optimization of a single end point. More often project teams are tasked with optimizing the primary activity while minimizing the effect on a secondary, selectivity target or on a critical ADMET parameter. We have therefore studied the application of the 3D activity cliff analysis to multiple activity endpoints. These �selectivity cliffs� highlight where molecular changes have a large effect on the activity against one target but not another. We will discuss the challenges of visualising this data and present some novel techniques to deal with this. 

Analysing selectivity through multi-dimensional activity cliff analysis

248th National Meeting (2014)

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