Computational evaluation of some indenopyrazole derivatives as anticancer compounds; application of QSAR and docking methodologies

Shahlaei, Mohsen; Fassihi, Afshin; Saghaie, Lotfollah; Arkan, Elham; Madadkar-Sobhani, Armin; Pourhossein, Alireza

doi:10.3109/14756366.2011.618991

Cited by 18 publications

(6 citation statements)

References 47 publications

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“…Structure-based approaches which are also called protein-based studies are the most useful methods when the structure of the receptor, usually a protein, is available. They deal with modeling the ligand-receptor interactions through docking (Vitale et al, 2013;Ferreira et al, 2013;Shahlaei et al, 2013) and a more accurate method, molecular dynamics (MD) simulation (Hucke et al, 2014;Chavan et al, 2011;Shahlaei et al, 2011). Such studies provide information on possible types of interaction forces between the ligand and its receptor and also information on the nature of the receptor.…”

Section: Introductionmentioning

confidence: 99%

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QSAR and docking studies of some 1,2,3,4-tetrahydropyrimidines: evaluation of gp41 as possible target for anti-HIV-1 activity

et al. 2014

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Inhibition of gp41 protein was proposed as a possible mechanism for the anti-HIV-1 properties of some 4-aryl-1,2,3,4-tetrahydropyrimidine-2(1H)-one (thione) derivatives. Two different in silico approaches, namely quantitative structure activity relationship (QSAR) and molecular docking studies were performed to characterize the relation between the structural features and the anti-HIV-1 activity and investigating the mode of interaction of the compounds with gp41. In the QSAR approach, free least squares support vector machine was used to derive a non-linear model based on the most important descriptors responsible for the activity of the compounds selected by stepwise multiple linear regression method. Docking results proved that the studied molecules have the optimum key interactions including hydrogen bonding, hydrophobic, electrostatic, p-p and cation-p interactions with the specific inhibitor binding site of gp41.

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Section: Introductionmentioning

confidence: 99%

“…The use of QSAR methods is also limited in understanding the mechanism of action of the studied molecule. Hence, the application of the two approaches simultaneously helps medicinal chemist to design more potent ligands as modulators of biological targets (Shahlaei et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

QSAR and docking studies of some 1,2,3,4-tetrahydropyrimidines: evaluation of gp41 as possible target for anti-HIV-1 activity

et al. 2014

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“…For this case, the predicted CLX concentration of each sample in the prediction step was compared with the experimental CLX concentration [30][31][32].…”

Section: Statistical Parametersmentioning

confidence: 99%

Application of unfolded principal component analysis–radial basis function neural network for determination of celecoxib in human serum by three-dimensional excitation–emission matrix fluorescence spectroscopy

Shahlaei

Bahrami

Abdolmaleki

et al. 2015

Spectrochimica Acta Part A: Molecular and Biomolecular Spectros

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“…Quantitative structure–activity relationship (QSAR) as an important part of drug design and discovery relates structural features to biological activity . The ultimate goal of formulating the QSAR is to develop mathematical models estimating the biological activities by relying on the assumption that these are determined solely by the molecular structures.…”

Section: Introductionmentioning

confidence: 99%

“…It is regularly used where datasets of interest are large and complicated to interpret directly, and where complex interrelationships between independent variables are not easy to recognize and visualize. Such conditions can be seen in a typical QSAR study where large number of theoretical descriptors can be calculated . These new generated variables, which are called principal components (PCs), are linear combinations of the original descriptors and sorted so that they explain as much of the original data's variance as possible.…”

Section: Introductionmentioning

confidence: 99%

The applications of PCA in QSAR studies: A case study on CCR5 antagonists

Shahlaei

2017

Chem Biol Drug Des

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Principal component analysis (PCA), as a well-known multivariate data analysis and data reduction technique, is an important and useful algebraic tool in drug design and discovery. PCA, in a typical quantitative structure-activity relationship (QSAR) study, analyzes an original data matrix in which molecules are described by several intercorrelated quantitative dependent variables (molecular descriptors). Although extensively applied, there is disparity in the literature with respect to the applications of PCA in the QSAR studies. This study investigates the different applications of PCA in QSAR studies using a dataset including CCR5 inhibitors. The different types of preprocessing are used to compare the PCA performances. The use of PC plots in the exploratory investigation of matrix of descriptors is described. This work is also proved PCA analysis to be a powerful technique for exploring complex datasets in QSAR studies for identification of outliers. This study shows that PCA is able to easily apply to the pool of calculated structural descriptors and also the extracted information can be used to help decide upon an appropriate harder model for further analysis.

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Computational evaluation of some indenopyrazole derivatives as anticancer compounds; application of QSAR and docking methodologies

Cited by 18 publications

References 47 publications

QSAR and docking studies of some 1,2,3,4-tetrahydropyrimidines: evaluation of gp41 as possible target for anti-HIV-1 activity

QSAR and docking studies of some 1,2,3,4-tetrahydropyrimidines: evaluation of gp41 as possible target for anti-HIV-1 activity

Application of unfolded principal component analysis–radial basis function neural network for determination of celecoxib in human serum by three-dimensional excitation–emission matrix fluorescence spectroscopy

The applications of PCA in QSAR studies: A case study on CCR5 antagonists

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