Literature DB >> 28975527

The conformation-independent QSPR approach for predicting the oxidation rate constant of water micropollutants.

Erlinda V Ortiz1, Daniel O Bennardi2, Daniel E Bacelo3, Silvina E Fioressi3, Pablo R Duchowicz4.   

Abstract

In advanced water treatment processes, the degradation efficiency of contaminants depends on the reactivity of the hydroxyl radical toward a target micropollutant. The present study predicts the hydroxyl radical rate constant in water (k OH) for 118 emerging micropollutants, by means of quantitative structure-property relationships (QSPR). The conformation-independent QSPR approach is employed, together with a large number of 15,251 molecular descriptors derived with the PaDEL, Epi Suite, and Mold2 freewares. The best multivariable linear regression (MLR) models are found with the replacement method variable subset selection technique. The proposed five-descriptor model has the following statistics for the training set: [Formula: see text], RMS train = 0.21, while for the test set is [Formula: see text], RMS test = 0.11. This QSPR serves as a rational guide for predicting oxidation processes of micropollutants.

Entities:  

Keywords:  Molecular descriptors; Quantitative structure-property relationships; Reaction rate constant; Replacement method; Water micropollutant

Mesh:

Substances:

Year:  2017        PMID: 28975527     DOI: 10.1007/s11356-017-0315-5

Source DB:  PubMed          Journal:  Environ Sci Pollut Res Int        ISSN: 0944-1344            Impact factor:   4.223


  27 in total

1.  QSAR models for the removal of organic micropollutants in four different river water matrices.

Authors:  Sairam Sudhakaran; James Calvin; Gary L Amy
Journal:  Chemosphere       Date:  2012-01-14       Impact factor: 7.086

2.  CORAL: QSPR modeling of rate constants of reactions between organic aromatic pollutants and hydroxyl radical.

Authors:  A A Toropov; A P Toropova; B F Rasulev; E Benfenati; G Gini; D Leszczynska; J Leszczynski
Journal:  J Comput Chem       Date:  2012-05-28       Impact factor: 3.376

3.  Quantitative structure-property relationship analysis for the retention index of fragrance-like compounds on a polar stationary phase.

Authors:  Cristian Rojas; Pablo R Duchowicz; Piercosimo Tripaldi; Reinaldo Pis Diez
Journal:  J Chromatogr A       Date:  2015-10-22       Impact factor: 4.759

4.  y-Randomization and its variants in QSPR/QSAR.

Authors:  Christoph Rücker; Gerta Rücker; Markus Meringer
Journal:  J Chem Inf Model       Date:  2007-09-20       Impact factor: 4.956

5.  Prediction of rate constants for radical degradation of aromatic pollutants in water matrix: a QSAR study.

Authors:  Hrvoje Kusić; Bakhtiyor Rasulev; Danuta Leszczynska; Jerzy Leszczynski; Natalija Koprivanac
Journal:  Chemosphere       Date:  2009-02-07       Impact factor: 7.086

6.  Comparative chemometric modeling of cytochrome 3A4 inhibitory activity of structurally diverse compounds using stepwise MLR, FA-MLR, PLS, GFA, G/PLS and ANN techniques.

Authors:  Kunal Roy; Partha Pratim Roy
Journal:  Eur J Med Chem       Date:  2008-12-16       Impact factor: 6.514

7.  Predicting the reaction rate constants of micropollutants with hydroxyl radicals in water using QSPR modeling.

Authors:  Xiaohui Jin; Sigrid Peldszus; Peter M Huck
Journal:  Chemosphere       Date:  2015-05-22       Impact factor: 7.086

8.  A study of enhanced performance of VUV/UV process for the degradation of micropollutants from contaminated water.

Authors:  Mehdi Bagheri; Madjid Mohseni
Journal:  J Hazard Mater       Date:  2015-03-18       Impact factor: 10.588

9.  QSAR study for carcinogenicity in a large set of organic compounds.

Authors:  Pablo R Duchowicz; Nieves C Comelli; Erlinda V Ortiz; Eduardo A Castro
Journal:  Curr Drug Saf       Date:  2012-09

10.  Conformation-Independent QSPR Approach for the Soil Sorption Coefficient of Heterogeneous Compounds.

Authors:  José F Aranda; Juan C Garro Martinez; Eduardo A Castro; Pablo R Duchowicz
Journal:  Int J Mol Sci       Date:  2016-08-03       Impact factor: 5.923

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  1 in total

1.  Linear Regression QSAR Models for Polo-Like Kinase-1 Inhibitors.

Authors:  Pablo R Duchowicz
Journal:  Cells       Date:  2018-02-14       Impact factor: 6.600

  1 in total

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