scholarly journals 2D-QSAR Study of a Series of Pyrazoline-Based Anti-Tubercular Agents Using Genetic Function Approximation

2015 ◽  
Vol 03 (04) ◽  
pp. 45-53 ◽  
Author(s):  
Hemal M. Soni ◽  
Popatbhai K. Patel ◽  
Mahesh T. Chhabria ◽  
Dharmraj N. Rana ◽  
Bhushan M. Mahajan ◽  
...  
Keyword(s):  
Function Approximation ◽  
Genetic Function Approximation ◽  
Qsar Study ◽  
Genetic Function ◽  
2D Qsar

QSAR studies for the computational prediction of HMG-CoA reductase inhibitors by genetic function approximation technique

2013 ◽  
Vol 91 (4) ◽  
pp. 263-274 ◽  
Author(s):  
Mohamed K. Awad ◽  
Eman A. El-Bastawissy ◽  
Faten M. Atlam
Keyword(s):  
Biological Activity ◽  
Correlation Coefficient ◽  
Function Approximation ◽  
Approximation Technique ◽  
Genetic Function Approximation ◽  
Qsar Study ◽  
Genetic Function ◽  
2D Qsar ◽  
Qsar Models ◽  
Coa Reductase

Two-dimensional quantitative structure−activity relationship (2D-QSAR) models are useful in understanding how chemical structure is related to the biological activity of natural and synthetic chemicals. Also, they could be usefully employed for designing newer and better therapeutics. A 2D-QSAR study was performed for 52 compounds of a series of thiophenyl quinolines and α-asarone derivatives as potential hypocholesterolemic inhibitors using different types of physicochemical descriptors, which correlated significantly with the activity. Linear QSAR models were developed using multiple linear regression, where the genetic algorithm (genetic function approximation technique) was adopted for selecting the most appropriate descriptors. The results are discussed on the basis of regression data and the cross-validation technique. Model A is the best 2D-QSAR model describing the inhibition efficiency of HMG-CoA reductase with cross-validated squared correlation coefficient (Q 2 = 0.700) and the squared correlation coefficient (R 2 = 0.752), which is able to describe 70% of the variance in the experimental activity. The good agreement between the experimental and the predicted values of pIC50 (micromoles per litre) (R = 0.876) confirms the reliability and the predictability of the proposed model. The results obtained from the present QSAR study explained the importance of the electronic, structural, spatial, and electrotopological descriptors in enhancing the biological activity of the investigated inhibitors.

QSAR Study on Imidazole Derivatives as Corrosion Inhibitors by Genetic Function Approximation Method

2016 ◽  
Vol 850 ◽  
pp. 426-432 ◽  
Author(s):  
Sang Xiong ◽  
Jian Lin Sun ◽  
Yang Xu ◽  
Xu Dong Yan
Keyword(s):  
Corrosion Inhibition ◽  
Function Approximation ◽  
Inhibition Efficiency ◽  
Topological Descriptors ◽  
Genetic Function Approximation ◽  
Qsar Study ◽  
Zagreb Index ◽  
Imidazole Derivatives ◽  
Genetic Function ◽  
Corrosion Inhibition Efficiency

Quantitative structure and activity relationship (QSAR) method is becoming more desirable for predicting of corrosion inhibition properties. The inhibition efficiency of organic compounds is dependent on many basic molecular descriptors, including structural descriptors, thermodynamic descriptors, information content descriptors, topological descriptors as Wiener index, Zagreb index and molecular connectivity indices. A genetic function approximation approach was used to run the regression analysis and establish correlations between different types of descriptors and measured corrosion inhibition efficiency for imidazole derivatives. A QSAR equation was developed and used to predict the corrosion inhibition efficiency for 18 imidazole derivatives. The prediction of corrosion efficiencies of these compounds nicely matched the experimental measurements.

Sign in / Sign up

Export Citation Format

Share Document