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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Article
Inhibition of Microbial Growth by Anilines: A QSAR Study
Author(s)
Ahmed Bouaoune, Leila Lourici, Hamza Haddag and Djelloul Messadi
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DOI:10.17265/2162-5298/2012.05.011
Affiliation(s)
ABSTRACT
The relative toxicity of 48 anilines using the Tetrahymena
pyriformis population growth characteristics 
(concentration causing 50% growth inhibition), available in
the literature, was studied. At first, the entire data set was randomly split
into a training set (31 chemicals) used to establish the QSAR model, and a test
set (17 chemicals) for statistical external validation. A biparametric model
was developed using, as independent variables, 3D theoretical descriptors
derived from DRAGON software. The GA-MLR (genetic algorithm variable subset
selection) procedure
was performed on the trainingset by the software mobydigs using the OLS (ordinary
least squares) regression method, and GA(genetic algorithm)-VSS(variable
subset selection) by maximising the cross-validated explained variance (
). The obtained model was examined for robustness (
cross-validation, Y-scrambling) and predictive ability
through both internal (
, bootstrap) and external validation (
) methods. Descriptors included in the QSAR model
indicated that 
value was related to molecular size and shape, and
interaction of molecule with its surrounding medium or its target. Moreover,
the applicability domain of the model was discussed.
(concentration causing 50% growth inhibition), available in
the literature, was studied. At first, the entire data set was randomly split
into a training set (31 chemicals) used to establish the QSAR model, and a test
set (17 chemicals) for statistical external validation. A biparametric model
was developed using, as independent variables, 3D theoretical descriptors
derived from DRAGON software. The GA-MLR (genetic algorithm variable subset
selection) procedure
was performed on the trainingset by the software mobydigs using the OLS (ordinary
least squares) regression method, and GA(genetic algorithm)-VSS(variable
subset selection) by maximising the cross-validated explained variance (). The obtained model was examined for robustness (cross-validation, Y-scrambling) and predictive ability
through both internal (, bootstrap) and external validation () methods. Descriptors included in the QSAR model
indicated that value was related to molecular size and shape, and
interaction of molecule with its surrounding medium or its target. Moreover,
the applicability domain of the model was discussed.
KEYWORDS
Toxic agents, growth of microbial species, QSAR hybrid model, statistical external validation, applicability domain.
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