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.