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QSAR modelling of water quality indices of alkylphenol pollutants

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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,  [image omitted] = 0.854 for log (1/BOD) model on 24 APs and r2 = 0.888,  [image omitted] = 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: r2 = 0.889,  [image omitted] = 0.848 for log (1/BOD5) and r2 = 0.885,  [image omitted] = 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.
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Keywords: Alkylphenols; BOD5; COD; MLR; QSAR

Document Type: Research Article

Affiliations: 1: Department of Health Science, School of Natural Science, Dongduk Women's University, Seoul, Korea 2: QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Structural and Functional Biology (DBSF), University of Insubria, Varese, Italy 3: Gyeonggi-do Public Health and Enviroment Research Institute 324-1 Pajang-dong, Gyeonggi, Korea 4: Department of Data and Information, Division of Computer Science and Information, Dongduk Women's University, Seoul, Korea 5: Department of Environmental and Biomolecular Systems, Oregon Health and Science University, Beaverton, OR, USA

Publication date: December 1, 2007

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