Density Functional Theory-based Quantitative Structure Activity Relationship (QSAR) Study of Alkanol and Alkanthiol Derivatives

2009 ◽  
Vol 28 (5) ◽  
pp. 568-575 ◽  
Author(s):  
Ahmad Reza Mehdipour ◽  
Mohammad Ali Safarpour ◽  
Fariba Taghavi ◽  
Maryam Jamali
Keyword(s):  
Density Functional Theory ◽  
Density Functional ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Qsar Study ◽  
Functional Theory ◽  
Structure Activity

scholarly journals Predictive Quantitative Structure–Activity Relationship Modeling of the Antifungal and Antibiotic Properties of Triazolothiadiazine Compounds

2020 ◽  
Vol 4 (1) ◽  
pp. 2
Author(s):  
Michael Appell ◽  
David L. Compton ◽  
Kervin O. Evans
Keyword(s):  
Density Functional Theory ◽  
Predictive Models ◽  
Density Functional ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Two Dimensional ◽  
Quantitative Structure ◽  
Functional Theory ◽  
Structure Activity

Predictive models were developed using two-dimensional quantitative structure activity relationship (QSAR) methods coupled with B3LYP/6-311+G** density functional theory modeling that describe the antimicrobial properties of twenty-four triazolothiadiazine compounds against Aspergillus niger, Aspergillus flavus and Penicillium sp., as well as the bacteria Staphylococcus aureus, Bacillus subtilis, Escherichia coli, and Pseudomonas aeruginosa. B3LYP/6-311+G** density functional theory calculations indicated the triazolothiadiazine derivatives possess only modest variation between the frontier orbital properties. Genetic function approximation (GFA) analysis identified the topological and density functional theory derived descriptors for antimicrobial models using a population of 200 models with one to three descriptors that were crossed for 10,000 generations. Two or three descriptor models provided validated predictive models for antifungal and antibiotic properties with R2 values between 0.725 and 0.768 and no outliers. The best models to describe antimicrobial activities include descriptors related to connectivity, electronegativity, polarizability, and van der Waals properties. The reported method provided robust two-dimensional QSAR models with topological and density functional theory descriptors that explain a variety of antifungal and antibiotic activities for structurally related heterocyclic compounds.

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.

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.

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