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A QSAR Study Based on SVM for the Compound of Hydroxyl Benzoic Esters
Hydroxyl benzoic esters are preservative, being widely used in food, medicine, and cosmetics. To explore the relationship between the molecular structure and antibacterial activity of these compounds and predict the compounds with similar structures, Quantitative Structure-Activity Relationship (QSA...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5512106/ https://www.ncbi.nlm.nih.gov/pubmed/28757813 http://dx.doi.org/10.1155/2017/4914272 |
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author | Wen, Li Li, Qing Li, Wei Cai, Qiao Cai, Yong-Ming |
author_facet | Wen, Li Li, Qing Li, Wei Cai, Qiao Cai, Yong-Ming |
author_sort | Wen, Li |
collection | PubMed |
description | Hydroxyl benzoic esters are preservative, being widely used in food, medicine, and cosmetics. To explore the relationship between the molecular structure and antibacterial activity of these compounds and predict the compounds with similar structures, Quantitative Structure-Activity Relationship (QSAR) models of 25 kinds of hydroxyl benzoic esters with the quantum chemical parameters and molecular connectivity indexes are built based on support vector machine (SVM) by using R language. The External Standard Deviation Error of Prediction (SDEP(ext)), fitting correlation coefficient (R(2)), and leave-one-out cross-validation (Q(2)(LOO)) are used to value the reliability, stability, and predictive ability of models. The results show that R(2) and Q(2)(LOO) of 4 kinds of nonlinear models are more than 0.6 and SDEP(ext) is 0.213, 0.222, 0.189, and 0.218, respectively. Compared with the multiple linear regression (MLR) model (R(2) = 0.421, RSD = 0.260), the correlation coefficient and the standard deviation are both better than MLR. The reliability, stability, robustness, and external predictive ability of models are good, particularly of the model of linear kernel function and eps-regression type. This model can predict the antimicrobial activity of the compounds with similar structure in the applicability domain. |
format | Online Article Text |
id | pubmed-5512106 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-55121062017-07-30 A QSAR Study Based on SVM for the Compound of Hydroxyl Benzoic Esters Wen, Li Li, Qing Li, Wei Cai, Qiao Cai, Yong-Ming Bioinorg Chem Appl Research Article Hydroxyl benzoic esters are preservative, being widely used in food, medicine, and cosmetics. To explore the relationship between the molecular structure and antibacterial activity of these compounds and predict the compounds with similar structures, Quantitative Structure-Activity Relationship (QSAR) models of 25 kinds of hydroxyl benzoic esters with the quantum chemical parameters and molecular connectivity indexes are built based on support vector machine (SVM) by using R language. The External Standard Deviation Error of Prediction (SDEP(ext)), fitting correlation coefficient (R(2)), and leave-one-out cross-validation (Q(2)(LOO)) are used to value the reliability, stability, and predictive ability of models. The results show that R(2) and Q(2)(LOO) of 4 kinds of nonlinear models are more than 0.6 and SDEP(ext) is 0.213, 0.222, 0.189, and 0.218, respectively. Compared with the multiple linear regression (MLR) model (R(2) = 0.421, RSD = 0.260), the correlation coefficient and the standard deviation are both better than MLR. The reliability, stability, robustness, and external predictive ability of models are good, particularly of the model of linear kernel function and eps-regression type. This model can predict the antimicrobial activity of the compounds with similar structure in the applicability domain. Hindawi 2017 2017-07-03 /pmc/articles/PMC5512106/ /pubmed/28757813 http://dx.doi.org/10.1155/2017/4914272 Text en Copyright © 2017 Li Wen et al. https://creativecommons.org/licenses/by/4.0/ 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. |
spellingShingle | Research Article Wen, Li Li, Qing Li, Wei Cai, Qiao Cai, Yong-Ming A QSAR Study Based on SVM for the Compound of Hydroxyl Benzoic Esters |
title | A QSAR Study Based on SVM for the Compound of Hydroxyl Benzoic Esters |
title_full | A QSAR Study Based on SVM for the Compound of Hydroxyl Benzoic Esters |
title_fullStr | A QSAR Study Based on SVM for the Compound of Hydroxyl Benzoic Esters |
title_full_unstemmed | A QSAR Study Based on SVM for the Compound of Hydroxyl Benzoic Esters |
title_short | A QSAR Study Based on SVM for the Compound of Hydroxyl Benzoic Esters |
title_sort | qsar study based on svm for the compound of hydroxyl benzoic esters |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5512106/ https://www.ncbi.nlm.nih.gov/pubmed/28757813 http://dx.doi.org/10.1155/2017/4914272 |
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