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

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dc.contributor.author Ravikumar, Balaguru
dc.contributor.author Timonen, Sanna
dc.contributor.author Alam, Zaid
dc.contributor.author Parri, Elina
dc.contributor.author Wennerberg, Krister
dc.contributor.author Aittokallio, Tero
dc.date.accessioned 2020-11-21T00:02:13Z
dc.date.available 2021-12-18T03:45:09Z
dc.date.issued 2019-11-21
dc.identifier.citation 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
dc.identifier.other PURE: 128250076
dc.identifier.other PURE UUID: de49bf58-99ba-4208-ab33-1c3d23a3982c
dc.identifier.other WOS: 000498043500013
dc.identifier.other ORCID: /0000-0002-8139-5950/work/66033456
dc.identifier.other ORCID: /0000-0002-0886-9769/work/66034101
dc.identifier.other ORCID: /0000-0002-9144-5421/work/66034391
dc.identifier.other ORCID: /0000-0003-0500-533X/work/66034531
dc.identifier.uri http://hdl.handle.net/10138/321778
dc.description.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. en
dc.format.extent 21
dc.language.iso eng
dc.relation.ispartof Cell chemical biology
dc.rights.uri info:eu-repo/semantics/openAccess
dc.subject 3111 Biomedicine
dc.subject 113 Computer and information sciences
dc.subject KINASE INHIBITORS
dc.subject DISCOVERY
dc.subject POLYPHARMACOLOGY
dc.subject INFORMATION
dc.subject DERIVATIVES
dc.subject PREDICTION
dc.subject ENSEMBLE
dc.subject LIBRARY
dc.subject CANCER
dc.title Chemogenomic Analysis of the Druggable Kinome and Its Application to Repositioning and Lead Identification Studies en
dc.type Article
dc.contributor.organization Computational Systems Medicine
dc.contributor.organization Institute for Molecular Medicine Finland
dc.contributor.organization University of Helsinki
dc.contributor.organization Physiology and Neuroscience (-2020)
dc.contributor.organization Krister Wennerberg / Principal Investigator
dc.contributor.organization Helsinki Institute for Information Technology
dc.contributor.organization Tero Aittokallio / Principal Investigator
dc.contributor.organization Bioinformatics
dc.description.reviewstatus Peer reviewed
dc.relation.doi https://doi.org/10.1016/j.chembiol.2019.08.007
dc.relation.issn 1879-1301
dc.rights.accesslevel openAccess
dc.type.version draft

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