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Quantitative Structure–Activity Relationship Modeling of Kinase Selectivity Profiles

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...

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Autores principales: Kothiwale, Sandeepkumar, Borza, Corina, Pozzi, Ambra, Meiler, Jens
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6151389/
https://www.ncbi.nlm.nih.gov/pubmed/28925954
http://dx.doi.org/10.3390/molecules22091576
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author Kothiwale, Sandeepkumar
Borza, Corina
Pozzi, Ambra
Meiler, Jens
author_facet Kothiwale, Sandeepkumar
Borza, Corina
Pozzi, Ambra
Meiler, Jens
author_sort Kothiwale, Sandeepkumar
collection PubMed
description 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.
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spelling pubmed-61513892018-11-13 Quantitative Structure–Activity Relationship Modeling of Kinase Selectivity Profiles Kothiwale, Sandeepkumar Borza, Corina Pozzi, Ambra Meiler, Jens Molecules Article 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. MDPI 2017-09-19 /pmc/articles/PMC6151389/ /pubmed/28925954 http://dx.doi.org/10.3390/molecules22091576 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kothiwale, Sandeepkumar
Borza, Corina
Pozzi, Ambra
Meiler, Jens
Quantitative Structure–Activity Relationship Modeling of Kinase Selectivity Profiles
title Quantitative Structure–Activity Relationship Modeling of Kinase Selectivity Profiles
title_full Quantitative Structure–Activity Relationship Modeling of Kinase Selectivity Profiles
title_fullStr Quantitative Structure–Activity Relationship Modeling of Kinase Selectivity Profiles
title_full_unstemmed Quantitative Structure–Activity Relationship Modeling of Kinase Selectivity Profiles
title_short Quantitative Structure–Activity Relationship Modeling of Kinase Selectivity Profiles
title_sort quantitative structure–activity relationship modeling of kinase selectivity profiles
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6151389/
https://www.ncbi.nlm.nih.gov/pubmed/28925954
http://dx.doi.org/10.3390/molecules22091576
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