The chromatographic hydrophobicity index (CHI) is an HPLC-based parameter that provides reliable guidance in optimization of pharmacological efficiency and adsorption, distribution, metabolism and exertion (ADME) profile of drug candidates. In the present work, classical and three-dimensional quantitative structure-property relationship (QSPR) models were developed for prediction of CHI values of some 4-hydroxycoumarin analogs on immobilized artificial membrane column. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) as 3D-QSPR methods were performed to gain insight into the key structural factors affecting on the chromatographic hydrophobicity of interested chemicals. The calculated parameters of Q , R and standard error were 0.545, 0.996 and 0.773 for CoMFA model and 0.815, 0.986 and 1.44 for CoMSIA model, respectively. The contour maps for steric fields of the CoMFA model illustrate that the hydrophobicity of chemicals will be higher when the positions of R6, R7 and R8 in the 4-hydroxycuomarin ring are substituted by alkyl groups. Moreover, by the analysis of the plots of electrostatic fields, it was concluded that the CHI value greatly increases if one hydrogen on coumarin ring is substituted by the F, Cl, Br, OH or OCH group.
In this work, the atmospheric lifetime of 60 halocarbons was estimated from their theoretical derived molecular descriptors by applying quantitative structure–property relationship (QSPR) methodology. The most relevant descriptors selected by stepwise multiple linear regression were used for developing linear and nonlinear models by using multiple linear regression and support vector machine, respectively. Here we show that the support vector machine model is finely capable for predicting the lifetime of halocarbons. The built support vector machine model was assessed by leave one out cross-validation (Q2 = 0.928, SPRESS = 0.479) and Y-randomization test (R2 = 0.222 for 25 trail) as well as external validation test. The developed support vector machine model was used for prediction of the atmospheric lifetime of some halocarbons with lifetimes not reported experimentally.
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