Modeling corrosion inhibition of iron in acid medium by genetic function approximation method: A QSAR model

2011 ◽  
Vol 53 (11) ◽  
pp. 3457-3465 ◽  
Author(s):  
K.F. Khaled
Keyword(s):  
Acid Medium ◽  
Corrosion Inhibition ◽  
Approximation Method ◽  
Function Approximation ◽  
Qsar Model ◽  
Genetic Function Approximation ◽  
Genetic Function

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.

A Genetic-Function-Approximation-Based QSAR Model for the Affinity of Arylpiperazines toward α1Adrenoceptors

2006 ◽  
Vol 46 (3) ◽  
pp. 1466-1478 ◽  
Author(s):  
Laura Maccari ◽  
Matteo Magnani ◽  
Giovannella Strappaghetti ◽  
Federico Corelli ◽  
Maurizio Botta ◽  
...  
Keyword(s):  
Function Approximation ◽  
Qsar Model ◽  
Genetic Function Approximation ◽  
Genetic Function

scholarly journals A Genetic-Function-Approximation-Based QSAR Model for the Affinity of Arylpiperazines Toward α1 Adrenoceptors.

ChemInform ◽  
2006 ◽  
Vol 37 (31) ◽  
Author(s):  
Laura Maccari ◽  
Matteo Magnani ◽  
Giovannella Strappaghetti ◽  
Federico Corelli ◽  
Maurizio Botta ◽  
...  
Keyword(s):  
Function Approximation ◽  
Qsar Model ◽  
Genetic Function Approximation ◽  
Genetic Function

QSAR and Lead Optimization

2019 ◽  
pp. 80-100
Author(s):  
N. Ramalakshmi ◽  
S. Arunkumar ◽  
Sakthivel Balasubramaniyan
Keyword(s):  
Biological Activity ◽  
Regression Analysis ◽  
Drug Design ◽  
Physicochemical Properties ◽  
Statistical Methods ◽  
Qsar Model ◽  
Genetic Function Approximation ◽  
Statistical Parameters ◽  
Design And Optimization ◽  
Genetic Function

There are many diseases for which suitable drugs have not been identified. As the population increases and the environment gets polluted, new infections are reported. Random screening of synthesized compounds for biological activity is time consuming. QSAR has a prominent role in drug design and optimization. It is derived from the correlation between the physicochemical properties and biological activity. QSAR equations are generated using statistical methods like regression analysis and genetic function approximation. Both 2D parameters and 3D parameters are involved in generating the equation. Among several QSAR equations generated, the best ones are selected based on statistical parameters. Validation techniques usually verify the predictive power of generated QSAR equations. Once the developed QSAR model is validated to be good, the results of that model may be applied to predict the biological activity of newer analogues. This chapter illustrates the various steps in QSAR and describes the significance of statistical parameters and software used in QSAR.

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