QSAR modelling of water quality indices of alkylphenol pollutants.
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 (BOD(5)), 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: r(2) = 0.924, Q(2)(cv) =0.854 for log (1/BOD) model on 24 APs and r(2) = 0.888, Q(2)(cv) = 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(2)(cv) = 0.848 for log (1/BOD(5)) and r(2) = 0.885, Q(2)(cv) = 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.
Department of Health Science, School of Natural Science, Dongduk Women's University, Wolgog-dong 23-1, Sungbuk-gu, Seoul, Korea. kjhyon@dongduk.ac.kr
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JH
Kim
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Gramatica
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Kim
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Kim
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Tratnyek
D001673:Biodegradation, Environmental; D003198:Computer Simulation; D010636:Phenols / Q000378:metabolism*; D021281:Quantitative Structure-Activity Relationship; D012044:Regression Analysis; D014874:Water Pollutants, Chemical / Q000378:metabolism*; D018508:Water Purification
D010636:Phenols; D014874:Water Pollutants, Chemical