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 & 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.
Studies on the Purification of Silicon Tetrachloride with Organic Bases Such as Acid Amides, Ketones and Aniline Derivatives, etc.
