scholarly journals Development and Validation of Quantitative Structure-Activity Relationship Models for Compounds Acting on Serotoninergic Receptors

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Grażyna Żydek ◽  
Elżbieta Brzezińska
Keyword(s):  
Regression Analysis ◽  
Physicochemical Parameters ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Receptor Interaction ◽  
Regression Equations ◽  
Quantitative Structure ◽  
Receptor Affinity ◽  
Structure Activity

A quantitative structure-activity relationship (QSAR) study has been made on 20 compounds with serotonin (5-HT) receptor affinity. Thin-layer chromatographic (TLC) data and physicochemical parameters were applied in this study. RP2 TLC 60F254plates (silanized) impregnated with solutions of propionic acid, ethylbenzene, 4-ethylphenol, and propionamide (used as analogues of the key receptor amino acids) and their mixtures (denoted as S1–S7 biochromatographic models) were used in two developing phases as a model of drug-5-HT receptor interaction. The semiempirical method AM1 (HyperChem v. 7.0 program) and ACD/Labs v. 8.0 program were employed to calculate a set of physicochemical parameters for the investigated compounds. Correlation and multiple linear regression analysis were used to search for the best QSAR equations. The correlations obtained for the compounds studied represent their interactions with the proposed biochromatographic models. The good multivariate relationships (R2=0.78–0.84) obtained by means of regression analysis can be used for predicting the quantitative effect of biological activity of different compounds with 5-HT receptor affinity. “Leave-one-out” (LOO) and “leave-N-out” (LNO) cross-validation methods were used to judge the predictive power of final regression equations.

scholarly journals Modeling of Gas-Wall Partitioning of Organic Compounds Using a Quantitative Structure–Activity Relationship

2019 ◽  
Author(s):  
Sanghee Han ◽  
Myoseon Jang ◽  
Huanhuan Jiang
Keyword(s):  
Hydrogen Bond ◽  
Organic Compounds ◽  
Physicochemical Parameters ◽  
Polynomial Equation ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Structure Activity ◽  
The Impact

Abstract. This study streamlines modeling of the gas–wall process (GWP) of semivolatile organic compounds (SVOC) by predicting gas–wall equilibrium partitioning constant (Kw,i) and accommodation coefficient (αw,i) of SVOC(i) using a quantitative structure–activity relationship. PaDEL-Descriptor, software that calculates molecular descriptors, is employed to obtain physicochemical parameters (i.e., hydrogen bond acidity (Hd,i), hydrogen bond basicity (Ha,i), dipolarity/polarizability (Si), and polarizability (αi)) of SVOC(i). For the prediction of Kw,i, activity coefficients (γw,i) of SVOC(i) to the chamber wall are semiempirically predicted using chamber data in the form of a polynomial equation coupled with the physicochemical parameters. γw,i of various SVOCs differ in functionalities and molecular sizes ranging from 100 to 104. We conclude that the estimation of γw,i is essential to improve the prediction of Kw,i. To predict the impact of relative humidity (RH) on GWP, each coefficient in the polynomial equation for ln(Kw,i) was correlated to RH. Increasing RH enhanced GWP significantly for all polar SVOCs. For example, the predicted Kw,i of 1-heptanoic acid increased more than three times (from 0.58 to 1.96) by increasing RH from 0.4 to 0.75 due to the reduction in γw,i. The characteristic time for GWP are estimated using Kw,i and αw,i to evaluate the effect of GWP on secondary organic aerosol (SOA) mass. It might be significant in the absence of inorganic aerosol, but is insignificant in the presence of electrolytic salts, where aqueous reactions dominate SOA growth.

Prediction of Lipophilicity of some Quinolone Derivatives by using Quantitative Structure- Activity Relationship

2019 ◽  
Vol 16 ◽  
Author(s):  
Meysam Shirmohammadi ◽  
Zakiyeh Bayat ◽  
Esmat Mohammadinasab
Keyword(s):  
Partition Coefficient ◽  
Principal Component ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Selection Methods ◽  
Qsar Study ◽  
Structure Activity ◽  
Descriptor Selection

: Quantitative structure activity relationship (QSAR) was used to study the partition coefficient of some quinolones and their derivatives. These molecules are broad-spectrum antibiotic pharmaceutics. First, data were divided into two categories of train and test (validation) sets using random selection method. Second, three approaches including stepwise selection (STS) (forward), genetic algorithm (GA), and simulated annealing (SA) were used to select the descriptors, with the aim of examining the effect feature selection methods. To find the relation between descriptors and partition coefficient, multiple linear regression (MLR), principal component regression (PCR) and partial least squares (PLS) were used. QSAR study showed that the both regression and descriptor selection methods have vital role in the results. Different statistical metrics showed that the MLR-SA approach with (r2=0.96, q2=0.91, pred_r2=0.95) gives the best outcome. The proposed expression by MLR-SA approach can be used in the better design of novel quinolones and their derivatives.

Sign in / Sign up

Export Citation Format

Share Document