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

Research output: Contribution to journalArticleScientificpeer-review

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.

Original languageEnglish
JournalCell chemical biology
Volume26
Issue number11
Pages (from-to)1608-1622
Number of pages21
ISSN1879-1301
DOIs
Publication statusPublished - 21 Nov 2019
MoE publication typeA1 Journal article-refereed

Fields of Science

  • 3111 Biomedicine
  • 113 Computer and information sciences
  • KINASE INHIBITORS
  • DISCOVERY
  • POLYPHARMACOLOGY
  • INFORMATION
  • DERIVATIVES
  • PREDICTION
  • ENSEMBLE
  • LIBRARY
  • CANCER

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