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Ezqsar: An R Package for Developing QSAR Models Directly From Structures

BACKGROUND: Quantitative Structure Activity Relationship (QSAR) is a difficult computational chemistry approach for beginner scientists and a time consuming one for even more experienced researchers. METHOD AND MATERIALS: Ezqsar which is introduced here addresses both the issues. It considers import...

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Autor principal: Shamsara, Jamal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Bentham Open 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5748834/
https://www.ncbi.nlm.nih.gov/pubmed/29387275
http://dx.doi.org/10.2174/1874104501711010212
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author Shamsara, Jamal
author_facet Shamsara, Jamal
author_sort Shamsara, Jamal
collection PubMed
description BACKGROUND: Quantitative Structure Activity Relationship (QSAR) is a difficult computational chemistry approach for beginner scientists and a time consuming one for even more experienced researchers. METHOD AND MATERIALS: Ezqsar which is introduced here addresses both the issues. It considers important steps to have a reliable QSAR model. Besides calculation of descriptors using CDK library, highly correlated descriptors are removed, a provided data set is divided to train and test sets, descriptors are selected by a statistical method, statistical parameter for the model are presented and applicability domain is investigated. RESULTS: Finally, the model can be applied to predict the activities for an extra set of molecules for a purpose of either lead optimization or virtual screening. The performance is demonstrated by an example. CONCLUSION: The R package, ezqsar, is freely available via https://github.com/shamsaraj/ezqsar, and it runs on Linux and MS-Windows.
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spelling pubmed-57488342018-01-31 Ezqsar: An R Package for Developing QSAR Models Directly From Structures Shamsara, Jamal Open Med Chem J Article BACKGROUND: Quantitative Structure Activity Relationship (QSAR) is a difficult computational chemistry approach for beginner scientists and a time consuming one for even more experienced researchers. METHOD AND MATERIALS: Ezqsar which is introduced here addresses both the issues. It considers important steps to have a reliable QSAR model. Besides calculation of descriptors using CDK library, highly correlated descriptors are removed, a provided data set is divided to train and test sets, descriptors are selected by a statistical method, statistical parameter for the model are presented and applicability domain is investigated. RESULTS: Finally, the model can be applied to predict the activities for an extra set of molecules for a purpose of either lead optimization or virtual screening. The performance is demonstrated by an example. CONCLUSION: The R package, ezqsar, is freely available via https://github.com/shamsaraj/ezqsar, and it runs on Linux and MS-Windows. Bentham Open 2017-11-30 /pmc/articles/PMC5748834/ /pubmed/29387275 http://dx.doi.org/10.2174/1874104501711010212 Text en © 2017 Jamal Shamsara. https://creativecommons.org/licenses/by/4.0/legalcode This is an open access article distributed under the terms of the Creative Commons Attribution 4. 0 International Public License (CC-BY 4. 0), a copy of which is available at: https://creativecommons. org/licenses/by/4. 0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Article
Shamsara, Jamal
Ezqsar: An R Package for Developing QSAR Models Directly From Structures
title Ezqsar: An R Package for Developing QSAR Models Directly From Structures
title_full Ezqsar: An R Package for Developing QSAR Models Directly From Structures
title_fullStr Ezqsar: An R Package for Developing QSAR Models Directly From Structures
title_full_unstemmed Ezqsar: An R Package for Developing QSAR Models Directly From Structures
title_short Ezqsar: An R Package for Developing QSAR Models Directly From Structures
title_sort ezqsar: an r package for developing qsar models directly from structures
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5748834/
https://www.ncbi.nlm.nih.gov/pubmed/29387275
http://dx.doi.org/10.2174/1874104501711010212
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