QSAR modelling of water quality indices of alkylphenol pollutants

J. H. Kim; P. Gramatica; M. G. Kim; D. Kim; P. G. Tratnyek

doi:10.1080/10629360701698761

QSAR modelling of water quality indices of alkylphenol pollutants

J. H. Kim, P. Gramatica, M. G. Kim, D. Kim, P. G. Tratnyek

School Of Public Health

Research output: Contribution to journal › Article › peer-review

8 Scopus citations

Abstract

The aim of this study was to determine the degradability of 26 Alkylphenols (APs) by Chemical Oxygen Demand (COD) and/or 5-day Biochemical Oxygen Demand (BOD₅), and to describe these data from Quantitative Structure-activity Relationships (QSARs). Statistical analysis techniques, such as Multiple Linear Regression (MLR), Principal Component Regression (PCR), Partial Least-Squares (PLS) Regression and Neural Network (NN) were carried out to calibrate and validate four-descriptor QSAR models using two different types of descriptor sets. Stable MLR-QSAR models using Leave-One-Out (LOO) were obtained with high predictability performance: r² = 0.924, Q _cv² = 0.854 for log (1/BOD) model on 24 APs and r ² = 0.888, Q_cv² = 0.818 for log (1/COD) on all the studied APs. The MLR models, built with four Dragon descriptors selected by Genetic Algorithm (GA), presented the following performances on 24 APs: r ² = 0.889, Q_cv² = 0.848 for log (1/BOD5) and r² = 0.885, Q_cv² = 0.834 for log (1/COD) on 26 compounds. From these results, it is expected that the QSAR models generated could be successfully expanded to predict the biological and chemical activities of structurally diverse AP compounds.

Original language	English (US)
Pages (from-to)	729-743
Number of pages	15
Journal	SAR and QSAR in Environmental Research
Volume	18
Issue number	7-8
DOIs	https://doi.org/10.1080/10629360701698761
State	Published - Dec 2007

Keywords

Alkylphenols
BOD5
COD
MLR
QSAR

ASJC Scopus subject areas

Bioengineering
Molecular Medicine
Drug Discovery

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10.1080/10629360701698761

Cite this

@article{855f15a7be154daeb7b67c736773dbc4,

title = "QSAR modelling of water quality indices of alkylphenol pollutants",

abstract = "The aim of this study was to determine the degradability of 26 Alkylphenols (APs) by Chemical Oxygen Demand (COD) and/or 5-day Biochemical Oxygen Demand (BOD5), and to describe these data from Quantitative Structure-activity Relationships (QSARs). Statistical analysis techniques, such as Multiple Linear Regression (MLR), Principal Component Regression (PCR), Partial Least-Squares (PLS) Regression and Neural Network (NN) were carried out to calibrate and validate four-descriptor QSAR models using two different types of descriptor sets. Stable MLR-QSAR models using Leave-One-Out (LOO) were obtained with high predictability performance: r2 = 0.924, Q cv2 = 0.854 for log (1/BOD) model on 24 APs and r 2 = 0.888, Qcv2 = 0.818 for log (1/COD) on all the studied APs. The MLR models, built with four Dragon descriptors selected by Genetic Algorithm (GA), presented the following performances on 24 APs: r 2 = 0.889, Qcv2 = 0.848 for log (1/BOD5) and r2 = 0.885, Qcv2 = 0.834 for log (1/COD) on 26 compounds. From these results, it is expected that the QSAR models generated could be successfully expanded to predict the biological and chemical activities of structurally diverse AP compounds.",

keywords = "Alkylphenols, BOD5, COD, MLR, QSAR",

author = "Kim, {J. H.} and P. Gramatica and Kim, {M. G.} and D. Kim and Tratnyek, {P. G.}",

note = "Funding Information: yDepartment of Health Science, School of Natural Science, Dongduk Women{\textquoteright}s University, Wolgog-dong 23-1, Sungbuk-gu, 136-714, Seoul, Korea zQSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Structural and Functional Biology (DBSF), University of Insubria, Via J.H. Dunant 3 – 21100, Varese, Italy xGyeonggi-do Public Health and Enviroment Research Institute 324-1 Pajang-dong, Jangan-gu, Suwon-si, Gyeonggi, Korea {Department of Data and Information, Division of Computer Science and Information, Dongduk Women{\textquoteright}s University, Wolgog-dong 23-1, Sungbuk-gu, 136-714, Seoul, Korea kDepartment of Environmental and Biomolecular Systems, Oregon Health and Science University, Beaverton, OR 97006-8921, USA",

year = "2007",

month = dec,

doi = "10.1080/10629360701698761",

language = "English (US)",

volume = "18",

pages = "729--743",

journal = "SAR and QSAR in Environmental Research",

issn = "1062-936X",

publisher = "Taylor and Francis Ltd.",

number = "7-8",

}

TY - JOUR

T1 - QSAR modelling of water quality indices of alkylphenol pollutants

AU - Kim, J. H.

AU - Gramatica, P.

AU - Kim, M. G.

AU - Kim, D.

AU - Tratnyek, P. G.

N1 - Funding Information: yDepartment of Health Science, School of Natural Science, Dongduk Women’s University, Wolgog-dong 23-1, Sungbuk-gu, 136-714, Seoul, Korea zQSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Structural and Functional Biology (DBSF), University of Insubria, Via J.H. Dunant 3 – 21100, Varese, Italy xGyeonggi-do Public Health and Enviroment Research Institute 324-1 Pajang-dong, Jangan-gu, Suwon-si, Gyeonggi, Korea {Department of Data and Information, Division of Computer Science and Information, Dongduk Women’s University, Wolgog-dong 23-1, Sungbuk-gu, 136-714, Seoul, Korea kDepartment of Environmental and Biomolecular Systems, Oregon Health and Science University, Beaverton, OR 97006-8921, USA

PY - 2007/12

Y1 - 2007/12

N2 - The aim of this study was to determine the degradability of 26 Alkylphenols (APs) by Chemical Oxygen Demand (COD) and/or 5-day Biochemical Oxygen Demand (BOD5), and to describe these data from Quantitative Structure-activity Relationships (QSARs). Statistical analysis techniques, such as Multiple Linear Regression (MLR), Principal Component Regression (PCR), Partial Least-Squares (PLS) Regression and Neural Network (NN) were carried out to calibrate and validate four-descriptor QSAR models using two different types of descriptor sets. Stable MLR-QSAR models using Leave-One-Out (LOO) were obtained with high predictability performance: r2 = 0.924, Q cv2 = 0.854 for log (1/BOD) model on 24 APs and r 2 = 0.888, Qcv2 = 0.818 for log (1/COD) on all the studied APs. The MLR models, built with four Dragon descriptors selected by Genetic Algorithm (GA), presented the following performances on 24 APs: r 2 = 0.889, Qcv2 = 0.848 for log (1/BOD5) and r2 = 0.885, Qcv2 = 0.834 for log (1/COD) on 26 compounds. From these results, it is expected that the QSAR models generated could be successfully expanded to predict the biological and chemical activities of structurally diverse AP compounds.

AB - The aim of this study was to determine the degradability of 26 Alkylphenols (APs) by Chemical Oxygen Demand (COD) and/or 5-day Biochemical Oxygen Demand (BOD5), and to describe these data from Quantitative Structure-activity Relationships (QSARs). Statistical analysis techniques, such as Multiple Linear Regression (MLR), Principal Component Regression (PCR), Partial Least-Squares (PLS) Regression and Neural Network (NN) were carried out to calibrate and validate four-descriptor QSAR models using two different types of descriptor sets. Stable MLR-QSAR models using Leave-One-Out (LOO) were obtained with high predictability performance: r2 = 0.924, Q cv2 = 0.854 for log (1/BOD) model on 24 APs and r 2 = 0.888, Qcv2 = 0.818 for log (1/COD) on all the studied APs. The MLR models, built with four Dragon descriptors selected by Genetic Algorithm (GA), presented the following performances on 24 APs: r 2 = 0.889, Qcv2 = 0.848 for log (1/BOD5) and r2 = 0.885, Qcv2 = 0.834 for log (1/COD) on 26 compounds. From these results, it is expected that the QSAR models generated could be successfully expanded to predict the biological and chemical activities of structurally diverse AP compounds.

KW - Alkylphenols

KW - BOD5

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KW - QSAR

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JF - SAR and QSAR in Environmental Research

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ER -

QSAR modelling of water quality indices of alkylphenol pollutants

Abstract

Keywords

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