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Quantitative Structure-activity Relationship Analysis for Predicting Lipophilicity of Aniline Derivatives (Including some Pharmaceutical Compounds)

[ Vol. 22 , Issue. 5 ]

Author(s):

Morteza Rezaei, Esmat Mohammadinasab* and Tahere Momeni Esfahani   Pages 333 - 345 ( 13 )

Abstract:


Background: In this study, we used a hierarchical approach to develop quantitative structureactivity relationship (QSAR) models for modeling lipophilicity of a set of 81 aniline derivatives containing some pharmaceutical compounds.

Objective: The multiple linear regression (MLR), principal component regression (PCR) and partial least square regression (PLSR) methods were utilized to construct QSAR models.

Materials and Methods: Quantum mechanical calculations at the density functional theory level and 6- 311++G** basis set were carried out to obtain the optimized geometry and then, the comprehensive set of molecular descriptors was computed by using the Dragon software. Genetic algorithm (GA) was applied to select suitable descriptors which have the most correlation with lipophilicity of the studied compounds.

Results: It was identified that such descriptors as Barysz matrix (SEigZ), hydrophilicity factor (Hy), Moriguchi octanol-water partition coefficient (MLOGP), electrophilicity (ω/eV) van der Waals volume (vWV) and lethal concentration (LC50/molkg-1) are the best descriptors for QSAR modeling. The high correlation coefficients and the low prediction errors for MLR, PCR and PLSR methods confirmed good predictability of the three models.

Conclusion: In present study, the high correlation between experimental and predicted logP values of aniline derivatives indicated the validation and the good quality of the resulting three regression methods, but MLR regression procedure was a little better than the PCR and PLSR methods. It was concluded that the studied aniline derivatives are not hydrophilic compounds and this means these compounds hardly dissolve in water or an aqueous solvent.

Keywords:

Lipophilicity, genetic algorithm-multiple linear regression, partial least square regression, principal component regression, aniline derivatives, QSAR.

Affiliation:

Department of Chemistry, Arak Branch, Islamic Azad University, Arak, Department of Chemistry, Arak Branch, Islamic Azad University, Arak, Department of Chemistry, Arak Branch, Islamic Azad University, Arak



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