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

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.

2015 ◽  
Vol 03 (04) ◽  
pp. 45-53 ◽  
Author(s):  
Hemal M. Soni ◽  
Popatbhai K. Patel ◽  
Mahesh T. Chhabria ◽  
Dharmraj N. Rana ◽  
Bhushan M. Mahajan ◽  
...  

2016 ◽  
Vol 850 ◽  
pp. 426-432 ◽  
Author(s):  
Sang Xiong ◽  
Jian Lin Sun ◽  
Yang Xu ◽  
Xu Dong Yan

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.


2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Li Wen ◽  
Qing Li ◽  
Wei Li ◽  
Qiao Cai ◽  
Yong-Ming Cai

Hydroxyl benzoic esters are preservative, being widely used in food, medicine, and cosmetics. To explore the relationship between the molecular structure and antibacterial activity of these compounds and predict the compounds with similar structures, Quantitative Structure-Activity Relationship (QSAR) models of 25 kinds of hydroxyl benzoic esters with the quantum chemical parameters and molecular connectivity indexes are built based on support vector machine (SVM) by using R language. The External Standard Deviation Error of Prediction (SDEPext), fitting correlation coefficient (R2), and leave-one-out cross-validation (Q2LOO) are used to value the reliability, stability, and predictive ability of models. The results show that R2 and Q2LOO of 4 kinds of nonlinear models are more than 0.6 and SDEPext is 0.213, 0.222, 0.189, and 0.218, respectively. Compared with the multiple linear regression (MLR) model (R2=0.421, RSD = 0.260), the correlation coefficient and the standard deviation are both better than MLR. The reliability, stability, robustness, and external predictive ability of models are good, particularly of the model of linear kernel function and eps-regression type. This model can predict the antimicrobial activity of the compounds with similar structure in the applicability domain.


Sign in / Sign up

Export Citation Format

Share Document