Structural Relationship Study of Octanol-Water Partition Coefficient of Some Sulfa Drugs Using GA-MLR and GA-ANN Methods

(E-pub Ahead of Print)

Author(s): Etratsadat Dadfar, Fatemeh Shafiei*, Tahereh Momeni Isfahani.

Journal Name: Current Computer-Aided Drug Design

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Abstract:

Aim and Objective: Sulfonamides (sulfa drugs) are compounds with a wide range of biological activities and they are the basis of several groups of drugs. Quantitative structure – property relationship (QSPR) models derived to predict the logarithm of water/ 1-octanol partition coefficients (logP) of sulfa drugs.

Materials and Methods: A data set of 43 sulfa drugs was randomly divided into 3 groups: training, test and validation sets consisting of 70%, 15% and 15% of data point, respectively. A large number of molecular descriptors were calculated with Dragon software. The genetic algorithm -multiple linear regressions (GA-MLR) and genetic algorithm -artificial neural network (GA-ANN) were employed to give the QSPR models. The possible molecular geometries of sulfa drugs were optimized at B3LYP/6-31G* level with Gaussian 98 software. The molecular descriptors derived from the Dragon software were used to build a predictive model for prediction logP of mentioned compounds. The genetic algorithm (GA) method was applied to select the most relevant molecular descriptors.

Results: The R2 and MSE values of the MLR model were calculated 0.312 and 5.074 respectively. R2 coefficients of 0.9869, 0.9944 and 0.9601for the training, test and validation sets of the ANN model, respectively

Conclusion: Comparison of the results revealed that the application the GA-ANN method gave better results than GA-MLR method.

Keywords: QSPR, multiple linear regressions, back propagation neural network (BPNN), genetic algorithm, sulfa drugs.

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Article Details

(E-pub Ahead of Print)
DOI: 10.2174/1573409915666190301124714
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