Chemogenomic Analysis of the Druggable Kinome and Its Application to Repositioning and Lead Identification Studies

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http://hdl.handle.net/10138/321778

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Ravikumar , B , Timonen , S , Alam , Z , Parri , E , Wennerberg , K & Aittokallio , T 2019 , ' Chemogenomic Analysis of the Druggable Kinome and Its Application to Repositioning and Lead Identification Studies ' , Cell chemical biology , vol. 26 , no. 11 , pp. 1608-1622 . https://doi.org/10.1016/j.chembiol.2019.08.007

Title: Chemogenomic Analysis of the Druggable Kinome and Its Application to Repositioning and Lead Identification Studies
Author: Ravikumar, Balaguru; Timonen, Sanna; Alam, Zaid; Parri, Elina; Wennerberg, Krister; Aittokallio, Tero
Contributor: University of Helsinki, Computational Systems Medicine
University of Helsinki, Computational Systems Medicine
University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Krister Wennerberg / Principal Investigator
University of Helsinki, Helsinki Institute for Information Technology
Date: 2019-11-21
Number of pages: 21
Belongs to series: Cell chemical biology
ISSN: 1879-1301
URI: http://hdl.handle.net/10138/321778
Abstract: Owing 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 Virtual-KinomeProfiler, an efficient computational platform that captures distinct representations of chemical similarity space of the druggable kinome for various drug discovery endeavors. By using 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 with traditional single-dose biochemical screening, which demonstrates its potential to drastically expedite the kinome-specific drug discovery process.
Subject: 3111 Biomedicine
113 Computer and information sciences
KINASE INHIBITORS
DISCOVERY
POLYPHARMACOLOGY
INFORMATION
DERIVATIVES
PREDICTION
ENSEMBLE
LIBRARY
CANCER
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