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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
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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. |
format | Online Article Text |
id | pubmed-6151389 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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