Quantitative Structure–Activity Relationship Analysis of 4(3H)-Quinazolone Derivatives as Tyrosine Kinase Inhibitors by Multiple Linear Regression

2010 ◽  
Vol 25 (5) ◽  
pp. 559-562 ◽  
Author(s):  
Vikas Srivastava ◽  
Deepa Sinha ◽  
Anjani K. Tiwari ◽  
Himanshu Sharma ◽  
Raj Bala Sharma ◽  
...  
Keyword(s):  
Linear Regression ◽  
Tyrosine Kinase ◽  
Multiple Linear Regression ◽  
Tyrosine Kinase Inhibitors ◽  
Kinase Inhibitors ◽  
Quantitative Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Relationship Analysis ◽  
Structure Activity

scholarly journals A Quantitative Structure – Activity Relationship Study on the Estrogen Activities of Bisphenol a Analogs using Computational Chemistry Methods and Multiple Linear Regression Technique

2018 ◽  
Vol 34 (3) ◽  
Author(s):  
Vu Van Dat ◽  
Le Kim Long ◽  
Doan Van Phuc ◽  
Nguyen Hoang Trang
Keyword(s):  
Density Functional Theory ◽  
Linear Regression ◽  
Bisphenol A ◽  
Multiple Linear Regression ◽  
Density Functional ◽  
Quantitative Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Functional Theory ◽  
Structure Activity

This article presents the results of the QSAR study of bisphenol A and its analogs. Molecular-chemical analysis of these substances is performed on base of the Density Functional Theory (DFT), using the function 6-31 + G *. The calculation of the characteristic parameters of the structure is given by optimizing molecular structures, vibrational frequencies, the molecular orbital energies with reasonable accuracy. The obtained quantum parameters and known observable activities are used as input data for constructing the QSAR model, using the classical data processing method in statistical mathematics - the multivariable linear regression. The constructed model QSAR has R2> 0,9; and . The statistical parameters show that the model, constructing by method of multiple linear regression using the parameters of quantum chemistry can be used as a predictive model of the activity of estrogens for unexplored derivatives and BPA analogs with moderate reliability. Keywords Bisphenol A, Estrogen, Density Functional Theory, M06 hybridmeta - GGA functional, Quantitative structure – activity relationship, Multiple linear regression References Rezg R, El-Fazaa S, Gharbi N, Mornagui B (March 2014). "Bisphenol A and human chronic diseases: Current evidences, possible mechanisms, and future perspectives".Environment International 2014, 64, 83–90. [2] Melzer D, Rice NE, Lewis C, Henley WE, Galloway TS (2010). Zhang, Baohong, ed."Association of Urinary Bisphenol a Concentration with Heart Disease: Evidence from NHANES 2003/06". PLoS ONE 5 (1). [3] Manikkam, M.; Tracey, R.; Guerrero-Bosagna, C.; Skinner, M. K. (January 24, 2013). "Plastics derived endocrine disruptors (BPA, DEHP and DBP) induce epigenetic transgenerational inheritance of obesity, reproductive disease and sperm epimutations".PLoS ONE 8 (1). 1–16. [4] D.R. Doerge, N.C. Twaddle, M. Vanlandingham, R.P. Brown, J.W. Fisher, Toxicol. Appl. Pharmacol. 2011, 255, 261.[5] Ho SM, Tang WY, Belmonte de Frausto J, Prins GS (2006). "Developmental exposure to estradiol and bisphenol A increases susceptibility to prostate carcinogenesis and epigenetically regulates phosphodiesterase type 4 variant 4". Cancer Res. 66 (11): 5624–32. [6] Johanna R. Rochester and Ashley L. Bolden (2015 Jul) “Bisphenol S and F: A Systematic Review and Comparison of the Hormonal Activity of Bisphenol A Substitutes”. Environ Health Perspect123(7):643-50.[7] Kelly, P. C., William, A. T., Thomas, E. W., QSAR models of thein vitro estrogen activity of bisphenol A analogs, QSAR Comb.Sci., 2003, 22: 78―88.[8]. Frisch, M. J. T., G. W. et al , Gaussian 09, Revision D.01. Gaussian, Inc., Wallingford CT, 2009.[9]. Zhao, Y.; Truhlar, D., The M06 suite of density functionals for main group thermochemistry, thermochemical kinetics, noncovalent interactions, excited states, and transition elements: two new functionals and systematic testing of four M06-class functionals and 12 other functionals. Theor Chem Account 2008,120 (1-3), 215-241.

Quantitative Structure-Activity Relationship Study of Camptothecin Derivatives as Anticancer Drugs Using Molecular Descriptors

2019 ◽  
Vol 22 (6) ◽  
pp. 387-399 ◽  
Author(s):  
Neda Ahmadinejad ◽  
Fatemeh Shafiei
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Anticancer Drugs ◽  
Van Der Waals ◽  
Quantitative Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Test Set ◽  
Structure Activity ◽  
Qsar Models

Aim and Objective:A Quantitative Structure-Activity Relationship (QSAR) has been widely developed to derive a correlation between chemical structures of molecules to their known activities. In the present investigation, QSAR models have been carried out on 76 Camptothecin (CPT) derivatives as anticancer drugs to develop a robust model for the prediction of physicochemical properties.Materials and Methods:A training set of 60 structurally diverse CPT derivatives was used to construct QSAR models for the prediction of physiochemical parameters such as Van der Waals surface area (SvdW), Van der Waals Volume (VvdW), Molar Refractivity (MR) and Polarizability (α). The QSAR models were optimized using Multiple Linear Regression (MLR) analysis. A test set of 16 compounds was evaluated using the defined models.:The Genetic Algorithm And Multiple Linear Regression Analysis (GA-MLR) were used to select the descriptors derived from the Dragon software to generate the correlation models that relate the structural features to the studied properties.Results:QSAR models were used to delineate the important descriptors responsible for the properties of the CPT derivatives. The statistically significant QSAR models derived by GA-MLR analysis were validated by Leave-One-Out Cross-Validation (LOOCV) and test set validation methods. The multicollinearity and autocorrelation properties of the descriptors contributed in the models were tested by calculating the Variance Inflation Factor (VIF) and the Durbin–Watson (DW) statistics.Conclusion:The predictive ability of the models was found to be satisfactory. Thus, QSAR models derived from this study may be helpful for modeling and designing some new CPT derivatives and for predicting their activity.

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