Next Article in Journal
Sulfadiazine Salicylaldehyde-Based Schiff Bases: Synthesis, Antimicrobial Activity and Cytotoxicity
Previous Article in Journal
Transesterification Synthesis of Chloramphenicol Esters with the Lipase from Bacillus amyloliquefaciens
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
Molecules 2017, 22(9), 1576; doi:10.3390/molecules22091576

Quantitative Structure–Activity Relationship Modeling of Kinase Selectivity Profiles

1
Department of Chemistry, Center for Structural Biology, Institute of Chemical Biology Vanderbilt University, Nashville, TN 37232, USA
2
Department of Medicine, Division of Nephrology, Vanderbilt University, Nashville, TN 37232, USA
3
Department of Medicine, Veterans Affairs Hospital, Nashville, TN 37232, USA
*
Author to whom correspondence should be addressed.
Received: 18 August 2017 / Revised: 11 September 2017 / Accepted: 12 September 2017 / Published: 19 September 2017
View Full-Text   |   Download PDF [2698 KB, uploaded 19 September 2017]   |  

Abstract

The discovery of selective inhibitors of biological target proteins is the primary goal of many drug discovery campaigns. However, this goal has proven elusive, especially for inhibitors targeting the well-conserved orthosteric adenosine triphosphate (ATP) binding pocket of kinase enzymes. The human kinome is large and it is rather difficult to profile early lead compounds against around 500 targets to gain an upfront knowledge on selectivity. Further, selectivity can change drastically during derivatization of an initial lead compound. Here, we have introduced a computational model to support the profiling of compounds early in the drug discovery pipeline. On the basis of the extensive profiled activity of 70 kinase inhibitors against 379 kinases, including 81 tyrosine kinases, we developed a quantitative structure–activity relation (QSAR) model using artificial neural networks, to predict the activity of these kinase inhibitors against the panel of 379 kinases. The model’s performance in predicting activity ranges from 0.6 to 0.8 depending on the kinase, from the area under the curve (AUC) of the receiver operating characteristics (ROC). The profiler is available online at http://www.meilerlab.org/index.php/servers/show?s_id=23. View Full-Text
Keywords: kinase selectivity profile; quantitative structure–activity relation; BCL::Cheminfo; artificial neural networks kinase selectivity profile; quantitative structure–activity relation; BCL::Cheminfo; artificial neural networks
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Supplementary material

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Kothiwale, S.; Borza, C.; Pozzi, A.; Meiler, J. Quantitative Structure–Activity Relationship Modeling of Kinase Selectivity Profiles. Molecules 2017, 22, 1576.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]

Molecules EISSN 1420-3049 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top