Linear and nonlinear quantitative structure–property relationship models for solubility of some anthraquinone, anthrone and xanthone derivatives in supercritical carbon dioxide

Hemmateenejad, Bahram; Shamsipur, Mojtaba; Miri, Ramin; Elyasi, Maryam; Foroghinia, Farzaneh; Sharghi, Hashem

doi:10.1016/j.aca.2008.01.011

Cited by 23 publications

(7 citation statements)

References 49 publications

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“…Since the data splitting has a considerable influence on the final selected model, a combined data splitting-feature selection (CDFS) strategy was employed [ 19 ]. In the CDFS methodology, several subdivisions of calibration and validation set were made (10 times).…”

Section: Methodsmentioning

confidence: 99%

A study on quantitative structure–activity relationship and molecular docking of metalloproteinase inhibitors based on L-tyrosine scaffold

et al. 2015

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BackgroundMMP-2 enzyme is a kind of matrix metalloproteinases that digests the denatured collagens and gelatins. It is highly involved in the process of tumor invasion and has been considered as a promising target for cancer therapy. The structural requirements of an MMP-2 inhibitor are: (1) a functional group that binds the zinc ion, and (2) a functional group which interacts with the enzyme backbone and the side chains which undergo effective interactions with the enzyme subsites.MethodsIn the present study, a QSAR model was generated to screen new inhibitors of MMP-2 based on L–hydroxy tyrosine scaffold. Descriptors generation were done by Hyperchem 8, DRAGON and Gaussian98W programs. SPSS and MATLAB programs have been used for multiple linear regression (MLR) and genetic algorithm partial least squares (GA-PLS) analyses and for theoretical validation. Applicability domain of the model was performed to screen new compounds. The binding site potential of all inhibitors was verified by structure-based docking according to their binding energy and then the best inhibitors were selected.ResultsThe best QSAR models in MLR and GA-PLS were reported, with the square correlation coefficient for leave-one-out cross-validation (Q2LOO) larger than 0.921 and 0.900 respectively. The created MLR and GA-PLS models indicated the importance of molecular size, degree of branching, flexibility, shape, three-dimensional coordination of different atoms in a molecule in inhibitory activities against MMP-2.The docking study indicated that lipophilic and hydrogen bonding interactions among the inhibitors and the receptor are involved in a ligand-receptor interaction. The oxygen of carbonyl and sulfonyl groups is important for hydrogen bonds of ligand with Leu82 and Ala83. R2 and R3 substituents play a main role in hydrogen bonding interactions. R1 is sited in the hydrophobic pocket. Methylene group can help a ligand to be fitted in the lipophilic pocket, so two methylene groups are better than one. The Phenyl group can create a π-π interaction with Phe86.ConclusionsThe QSAR and docking analyses demonstrated to be helpful tools in the prediction of anti-cancer activities and a guide to the synthesis of new metalloproteinase inhibitors based on L-tyrosine scaffold.

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Section: Methodsmentioning

confidence: 99%

A study on quantitative structure–activity relationship and molecular docking of metalloproteinase inhibitors based on L-tyrosine scaffold

et al. 2015

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“…The PC-ANN model was the same as we reported previously [23,33]. In the same manner as PCR analysis, the data sets were classified into calibration (or training) and prediction sets (see Section 2.3), however, since ANN needs a validation set through learning procedure, 51 samples of calibration set was selected randomly as validation samples.…”

Section: Non-linear Modeling: Pc-annmentioning

confidence: 99%

“…Artificial neural networks (ANNs) as non-parametric non-linear modeling techniques have attracted increasing interest in the recent years [22,23]. Multilayer feedforward neural networks (MLF-ANN) trained with back-propagation learning algorithm become increasingly popular techniques [22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Highly correlating distance/connectivity-based topological indices

Shamsipur¹,

Ghavami²,

Sharghi³

et al. 2008

Journal of Molecular Graphics and Modelling

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“…Khayamian and Esteki [4] reported a similar WNN model for five polycyclic aromatic compounds. More recently, Hemmateenejad et al [3] published a neural network QSPR model for scCO 2 solubility of twenty-nine anthraquinone, anthrone and xanthone derivatives.…”

Section: Introductionmentioning

confidence: 99%

“…An application of a mathematical model to interpret the available dye solubility data sets is an important method for a time and money saving prediction of solubility of existing and future dye structures. Quantitative structure-property relationship (QSPR) modelling is a mathematical relationship that has been used extensively in the pharmaceutical and environmental industries to model a very diverse range of biological and physicochemical properties of organic compounds, including solubility prediction of organic compounds in supercritical solvents [2][3][4][5][6]. There are a number of ways of generating QSPR models by linking mathematical representations of molecular structure to the materials' property of interest.…”

Section: Introductionmentioning

confidence: 99%

Robust modelling of solubility in supercritical carbon dioxide using Bayesian methods

Tarasova

Burden

Gasteiger

et al. 2010

Journal of Molecular Graphics and Modelling

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Linear and nonlinear quantitative structure–property relationship models for solubility of some anthraquinone, anthrone and xanthone derivatives in supercritical carbon dioxide

Cited by 23 publications

References 49 publications

A study on quantitative structure–activity relationship and molecular docking of metalloproteinase inhibitors based on L-tyrosine scaffold

A study on quantitative structure–activity relationship and molecular docking of metalloproteinase inhibitors based on L-tyrosine scaffold

Highly correlating distance/connectivity-based topological indices

Robust modelling of solubility in supercritical carbon dioxide using Bayesian methods

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