Nosrat Madadi Mahani; Maryam Alibeigi
Abstract
A quantitative Structure-Activity Relationship (QSAR) model was applied to the prediction of the antimicrobial activity of 22 derivatives 2, 4, 6-s-triazine as anti-malarial agents. The antimicrobial activity of 22 2, 4, 6-s-triazine derivatives were modeled with the descriptors of quantum-chemical ...
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A quantitative Structure-Activity Relationship (QSAR) model was applied to the prediction of the antimicrobial activity of 22 derivatives 2, 4, 6-s-triazine as anti-malarial agents. The antimicrobial activity of 22 2, 4, 6-s-triazine derivatives were modeled with the descriptors of quantum-chemical calculations with density functional theory (DFT) method at B3LYP/6‒31G level and topological descriptors. This study was conducted using the multiple linear regressions (MLR), the partial least square analysis (PLS) and the principal component regression (PCR) method. Results displayed that the MLR method predicted of antimicrobial activity good enough. The best model, with six descriptors was selected. Also it indicates very good consistency towards data variations for the validation methods. The predicted values of antimicrobial activity are in suitable agreement with the experimental results. The obtained results suggested that the PLS method could be more helpful to predict the antimicrobial activity of 2, 4, 6-s-triazine derivatives. This study to be usable to predict the activity of other derivatives in the same groups.
Nosrat Madadi Mahani; Azra Horzadeh
Volume 5, Issue 2 , September 2018, , Pages 23-30
Abstract
A quantitative structure activity relationship analysis has been applied to a data set of 34 derivatives of 8-hydroxy-2-iminochromene with inhibitory activities for carbonyl reductase 1. Semi-empirical quantum chemical calculations at the AM1 level were used to find the geometry of the studied molecules. ...
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A quantitative structure activity relationship analysis has been applied to a data set of 34 derivatives of 8-hydroxy-2-iminochromene with inhibitory activities for carbonyl reductase 1. Semi-empirical quantum chemical calculations at the AM1 level were used to find the geometry of the studied molecules. Whole numbers of descriptors were calculated with Dragon software, and a subset of calculated descriptors was selected from 407 Dragon descriptors with the multiple linear regression (MLR), partial least square and principal component analysis methods. Results displayed that the MLR method predicted of activity good enough. The best model of MLR, with seven descriptors was selected. Also it indicates very good consistency towards data variations for the validation methods. The predicted values of activities are in suitable agreement with the experimental results. The obtained results suggested that the PLS method could be more helpful to predict the biological activity of iminochromene derivatives. This study is be useful to predict the activity of other compounds in the same derivatives.