Chemogenomic Analysis of the Druggable Kinome and Its Application to Repositioning and Lead Identification Studies
| Autor: | Zaid Alam, Elina Parri, Balaguru Ravikumar, Krister Wennerberg, Tero Aittokallio, Sanna Timonen |
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| Přispěvatelé: | Computational Systems Medicine, Institute for Molecular Medicine Finland, University of Helsinki, Physiology and Neuroscience (-2020), Krister Wennerberg / Principal Investigator, Helsinki Institute for Information Technology, Tero Aittokallio / Principal Investigator, Bioinformatics |
| Rok vydání: | 2019 |
| Předmět: |
False discovery rate
Support Vector Machine INFORMATION Computer science PREDICTION Clinical Biochemistry Druggability ENSEMBLE Computational biology Biology 01 natural sciences Biochemistry Small Molecule Libraries Lead (geology) 0502 economics and business Drug Discovery Humans Kinome Computer Simulation 050207 economics Molecular Biology POLYPHARMACOLOGY Protein Kinase Inhibitors Pharmacology Computational model 050208 finance Vascular Endothelial Growth Factor Receptor-1 010405 organic chemistry Drug discovery DERIVATIVES 05 social sciences KINASE INHIBITORS Drug Repositioning Chemical similarity 113 Computer and information sciences CANCER 0104 chemical sciences ErbB Receptors Drug repositioning ROC Curve Cheminformatics DISCOVERY Area Under Curve LIBRARY Molecular Medicine Identification (biology) 3111 Biomedicine Protein Kinases |
| Zdroj: | Cell chemical biology. 26(11) |
| ISSN: | 2451-9448 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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