A dynamic, focused screening strategy that utilized a limited but diversified set of target-specific compounds was explored as an efficient means for the identification of inhibitors of the protein kinase PDK1. Approximately 21,500 compounds, including a 19,000 molecule kinase-focused compound collection (KFCC), were screened at two concentrations to identify initial leads. The KFCC included several empirically-derived, general kinase libraries and molecules chosen by PDK1-specific virtual screens. As was expected, this initial screen mostly identified potent leads with limited novelty. In order to overcome this limitation, the data from the screen were used to drive several rounds of a customized iterative focused screening (IFS) campaign. A machine-learning technique was used to build a predictive model to identify compounds to be screened in subsequent rounds. Molecules deemed not to be novel were removed from the training set for the next round, which allowed this campaign to progressively walk away from the chemical space covered by the KFCC. This resulted in the identification of PDK1 inhibitors which are uniquely different from publicly known chemotypes after just three rounds of screenings. A retrospective analysis of this IFS approach against an ultrahigh throughput screen (uHTS) indicated that while uHTS is still the most prolific paradigm for lead identification, this dynamic, focused screening approach was successful in discovering novel scaffolds for a medicinal chemistry effort. Finally, a theoretical optimization suggested the dynamic, focused screening approaches could provide either a complementary or alternative approach to uHTS for the efficient and rapid lead identification.
Keywords: Kinase focused compound collection (KFCC), iterative focused screening (IFS), quantitative structure analysis relationship (QSAR), random forest (RF), ultra high throughput screening (uHTS), lead identification, structure-activity relationship (SAR), kinase inhibitor
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