Quantum Chemical Studies of Some Sulphanilamide Schiff Bases Inhibitor Activity Using QSAR Methods
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The different calculated quantum chemical descriptors by DFT method were used for prediction of some sulphanilamide Schiff bases inhibitor activity as a binding constant (log K). Multiple linear regression (MLR) and artificial neural network (ANN) were employed for developing the useful quantitative structure activity relationship (QSAR) model. The obtained results presented superiority of ANN model over the MLR one. The offering QSAR model is very easy to computation and Physico-Chemically interpretable. Sensitivity analysis was used to determine the relative importance of each descriptor in ANN model. The order of importance of each descriptor according to this analysis is: molecular volume, molecular weight and dipole moment, respectively. These descriptors appear good information related to different structure of sulphanilamide Schiff bases can participate in their inhibitor activity.
Abstract
The different calculated quantum chemical descriptors by DFT method were used for prediction of some sulphanilamide Schiff bases inhibitor activity as a binding constant (log K). Multiple linear regression (MLR) and artificial neural network (ANN) were employed for developing the useful quantitative structure activity relationship (QSAR) model. The obtained results presented superiority of ANN model over the MLR one. The offering QSAR model is very easy to computation and Physico-Chemically interpretable. Sensitivity analysis was used to determine the relative importance of each descriptor in ANN model. The order of importance of each descriptor according to this analysis is: molecular volume, molecular weight and dipole moment, respectively. These descriptors appear good information related to different structure of sulphanilamide Schiff bases can participate in their inhibitor activity.
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