Chemogenomic Analysis of the Druggable Kinome and Its Application to Repositioning and Lead Identification Studies
32 Pages Posted: 22 Mar 2019 Sneak Peek Status: Review CompleteMore...
Due to the intrinsic polypharmacological nature of most small-molecule kinase inhibitors, there is a need for computational models that enable systematic exploration of the chemogenomic landscape underlying druggable kinome toward more efficient kinome profiling strategies. We implemented VirtualKinomeProfiler, an efficient computational platform that captures distinct representations of chemical similarity space of the druggable kinome for various drug discovery endeavors. By employing the computational platform, we profiled approximately 37 million compound-kinase pairs and made predictions for 151,708 compounds in terms of their repositioning and lead molecule potential against 248 kinases simultaneously. Experimental testing with biochemical assays validated 51 of the predicted interactions, identifying 19 small-molecule inhibitors of EGFR, HCK, FLT1, and MSK1 protein kinases. The prediction model led to a 1.5-fold increase in precision and 2.8-fold decrease in false discovery rate, when compared to traditional single-dose biochemical screening, which demonstrates its potential to drastically expedite the kinome-specific drug discovery process.
Keywords: Chemogenomic analysis, Statistical and machine learning model, High-throughput virtual kinome profiling, Compound repositioning and lead identification, Cancer-specific drug discovery
Suggested Citation: Suggested Citation