Targeted Kinase Selectivity from Kinase Profiling Data

Kinase selectivity plays a major role in the design strategy of lead series and in the ultimate success of kinase drug discovery programs. Although profiling compounds against a large panel of protein kinases has become a standard part of modern drug discovery, data accumulated from these kinase panels may be underutilized for new kinase projects. We present a method that can be used to optimize the selectivity profile of a compound using historical kinase profiling data. This method proposes chemical transformations based on pairs of very similar compounds, which are both active against a desired target kinase and differ in activity against another kinase. We show that these transformations are transferable across scaffolds, thus making this tool valuable to exploit kinase profiling data for unrelated series of compounds.