A Simple, Robust and Efficient Computational Method for n-Octanol/Water Partition Coefficients of Substituted Aromatic Drugs

A Simple, Robust and Efficient Computational Method for n-Octanol/Water Partition Coefficients of Substituted Aromatic Drugs

Abstract In this paper, multiple linear regression (MLR) was used to build quantitative structure property relationship (QSPR) of n-octanol-water partition coefficient (logPo/w) of 195 substituted aromatic drugs. The molecular descriptors were calculated for each compound by the VLifeMDS. By applyin...

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Main Authors: Asrin Bahmani, Saadi Saaidpour, Amin Rostami
Format: Article
Language:English
Published: Nature Publishing Group 2017-07-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-017-05964-z
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spelling doaj-6d59dccda86445c8b3cee3244f64c8e52020-12-08T01:29:24ZengNature Publishing GroupScientific Reports2045-23222017-07-017111410.1038/s41598-017-05964-zA Simple, Robust and Efficient Computational Method for n-Octanol/Water Partition Coefficients of Substituted Aromatic DrugsAsrin Bahmani0Saadi Saaidpour1Amin Rostami2Department of Chemistry, Faculty of science, University of KurdistanDepartment of chemistry, Faculty of science, Islamic Azad University, Sanandaj BranchDepartment of Chemistry, Faculty of science, University of KurdistanAbstract In this paper, multiple linear regression (MLR) was used to build quantitative structure property relationship (QSPR) of n-octanol-water partition coefficient (logPo/w) of 195 substituted aromatic drugs. The molecular descriptors were calculated for each compound by the VLifeMDS. By applying genetic algorithm/multiple linear regressions (GA/MLR) the most relevant descriptors were selected to build a QSPR model. The robustness of the model was characterized by the statistical validation and applicability domain (AD). The prediction results from MLR are in good agreement with the experimental values. The R2 and Q2 LOO for MLR are 0.9433, 0.9341. The AD of the model was analyzed based on the Williams plot. The effects of different selected descriptors are described.https://doi.org/10.1038/s41598-017-05964-z
collection DOAJ
language English
format Article
sources DOAJ
author Asrin Bahmani
Saadi Saaidpour
Amin Rostami
spellingShingle Asrin Bahmani
Saadi Saaidpour
Amin Rostami
A Simple, Robust and Efficient Computational Method for n-Octanol/Water Partition Coefficients of Substituted Aromatic Drugs
Scientific Reports
author_facet Asrin Bahmani
Saadi Saaidpour
Amin Rostami
author_sort Asrin Bahmani
title A Simple, Robust and Efficient Computational Method for n-Octanol/Water Partition Coefficients of Substituted Aromatic Drugs
title_short A Simple, Robust and Efficient Computational Method for n-Octanol/Water Partition Coefficients of Substituted Aromatic Drugs
title_full A Simple, Robust and Efficient Computational Method for n-Octanol/Water Partition Coefficients of Substituted Aromatic Drugs
title_fullStr A Simple, Robust and Efficient Computational Method for n-Octanol/Water Partition Coefficients of Substituted Aromatic Drugs
title_full_unstemmed A Simple, Robust and Efficient Computational Method for n-Octanol/Water Partition Coefficients of Substituted Aromatic Drugs
title_sort simple, robust and efficient computational method for n-octanol/water partition coefficients of substituted aromatic drugs
publisher Nature Publishing Group
series Scientific Reports
issn 2045-2322
publishDate 2017-07-01
description Abstract In this paper, multiple linear regression (MLR) was used to build quantitative structure property relationship (QSPR) of n-octanol-water partition coefficient (logPo/w) of 195 substituted aromatic drugs. The molecular descriptors were calculated for each compound by the VLifeMDS. By applying genetic algorithm/multiple linear regressions (GA/MLR) the most relevant descriptors were selected to build a QSPR model. The robustness of the model was characterized by the statistical validation and applicability domain (AD). The prediction results from MLR are in good agreement with the experimental values. The R2 and Q2 LOO for MLR are 0.9433, 0.9341. The AD of the model was analyzed based on the Williams plot. The effects of different selected descriptors are described.
url https://doi.org/10.1038/s41598-017-05964-z
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