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Study of the Applicability Domain of the QSAR Classification Models by Means of the Rivality and Modelability Indexes
The reliability of a QSAR classification model depends on its capacity to achieve confident predictions of new compounds not considered in the building of the model. The results of this external validation process show the applicability domain (AD) of the QSAR model and, therefore, the robustness of...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6278359/ https://www.ncbi.nlm.nih.gov/pubmed/30356020 http://dx.doi.org/10.3390/molecules23112756 |
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author | Luque Ruiz, Irene Gómez-Nieto, Miguel Ángel |
author_facet | Luque Ruiz, Irene Gómez-Nieto, Miguel Ángel |
author_sort | Luque Ruiz, Irene |
collection | PubMed |
description | The reliability of a QSAR classification model depends on its capacity to achieve confident predictions of new compounds not considered in the building of the model. The results of this external validation process show the applicability domain (AD) of the QSAR model and, therefore, the robustness of the model to predict the property/activity of new molecules. In this paper we propose the use of the rivality and modelability indexes for the study of the characteristics of the datasets to be correctly modeled by a QSAR algorithm and to predict the reliability of the built model to prognosticate the property/activity of new molecules. The calculation of these indexes has a very low computational cost, not requiring the building of a model, thus being good tools for the analysis of the datasets in the first stages of the building of QSAR classification models. In our study, we have selected two benchmark datasets with similar number of molecules but with very different modelability and we have corroborated the capacity of the predictability of the rivality and modelability indexes regarding the classification models built using Support Vector Machine and Random Forest algorithms with 5-fold cross-validation and leave-one-out techniques. The results have shown the excellent ability of both indexes to predict outliers and the applicability domain of the QSAR classification models. In all cases, these values accurately predicted the statistic parameters of the QSAR models generated by the algorithms. |
format | Online Article Text |
id | pubmed-6278359 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-62783592018-12-13 Study of the Applicability Domain of the QSAR Classification Models by Means of the Rivality and Modelability Indexes Luque Ruiz, Irene Gómez-Nieto, Miguel Ángel Molecules Article The reliability of a QSAR classification model depends on its capacity to achieve confident predictions of new compounds not considered in the building of the model. The results of this external validation process show the applicability domain (AD) of the QSAR model and, therefore, the robustness of the model to predict the property/activity of new molecules. In this paper we propose the use of the rivality and modelability indexes for the study of the characteristics of the datasets to be correctly modeled by a QSAR algorithm and to predict the reliability of the built model to prognosticate the property/activity of new molecules. The calculation of these indexes has a very low computational cost, not requiring the building of a model, thus being good tools for the analysis of the datasets in the first stages of the building of QSAR classification models. In our study, we have selected two benchmark datasets with similar number of molecules but with very different modelability and we have corroborated the capacity of the predictability of the rivality and modelability indexes regarding the classification models built using Support Vector Machine and Random Forest algorithms with 5-fold cross-validation and leave-one-out techniques. The results have shown the excellent ability of both indexes to predict outliers and the applicability domain of the QSAR classification models. In all cases, these values accurately predicted the statistic parameters of the QSAR models generated by the algorithms. MDPI 2018-10-24 /pmc/articles/PMC6278359/ /pubmed/30356020 http://dx.doi.org/10.3390/molecules23112756 Text en © 2018 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 Luque Ruiz, Irene Gómez-Nieto, Miguel Ángel Study of the Applicability Domain of the QSAR Classification Models by Means of the Rivality and Modelability Indexes |
title | Study of the Applicability Domain of the QSAR Classification Models by Means of the Rivality and Modelability Indexes |
title_full | Study of the Applicability Domain of the QSAR Classification Models by Means of the Rivality and Modelability Indexes |
title_fullStr | Study of the Applicability Domain of the QSAR Classification Models by Means of the Rivality and Modelability Indexes |
title_full_unstemmed | Study of the Applicability Domain of the QSAR Classification Models by Means of the Rivality and Modelability Indexes |
title_short | Study of the Applicability Domain of the QSAR Classification Models by Means of the Rivality and Modelability Indexes |
title_sort | study of the applicability domain of the qsar classification models by means of the rivality and modelability indexes |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6278359/ https://www.ncbi.nlm.nih.gov/pubmed/30356020 http://dx.doi.org/10.3390/molecules23112756 |
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