A Theoretical Approach to Model and Predict the Adsorption Coefficients of Some Small Aromatic Molecules on Carbon Nanotube

Heidari, Afsane; Fatemi, M.

doi:10.1002/jccs.201600761

Cited by 11 publications

(1 citation statement)

References 16 publications

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“…DRAGON software is very important software for the calculation of a lot of descriptors including constitutional, 2D autocorrelations, topological, getaway, WHIM, geometrical, RDF, and 2D matrix‐based descriptors . They are derived from different theories and approaches with the aim of predicting the biological and physicochemical properties of molecules . A total of 1,729 molecular descriptors were calculated for selected sulfa drugs by the Talete srl, DRAGON for the Windows Version 5.4‐2006 package .…”

Section: Methodsmentioning

confidence: 99%

Prediction of some thermodynamic properties of sulfonamide drugs using genetic algorithm‐multiple linear regressions

Dadfar

Shafiei

2019

J Chinese Chemical Soc

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A quantitative structure-property relationship (QSPR) has been widely developed to derive a correlation between chemical structures of molecules and their known properties. In the present study, the applicability of various molecular descriptors is tested for the QSPR study on 60 sulfonamide drugs (sulfa drugs). A training set of 50 sulfa drugs is used to construct QSPR models for the prediction of thermodynamic properties such as enthalpy of formation (ΔH f /kJ mol −1 ) and Gibbs free energy (ΔG f /kJ mol −1 ). These properties are analyzed at the B3LYP/6-31G* level using Gaussian 98 software. The QSPR models were optimized using multiple linear regression (MLR) analysis. The Genetic algorithm and backward method were used to reduce the number of descriptors derived from the Dragon software. The multicollinearity and autocorrelation properties of the descriptors contributed in the models were tested by calculating the variance inflation factor (VIF), Pearson correlation coefficient (PCC), and the Durbin-Watson (DW) statistics. The predictive powers of the models are discussed using leave-one-out cross-validation (LOOCV) and external validation methods. The statistical coefficients of the models were found to be satisfactory. Thus, QSPR models derived from this study may be helpful for designing some new sulfa drugs and for predicting their properties.

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Section: Methodsmentioning

confidence: 99%

Prediction of some thermodynamic properties of sulfonamide drugs using genetic algorithm‐multiple linear regressions

Dadfar

Shafiei

2019

J Chinese Chemical Soc

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Comparative molecular field analysis and hologram quantitative structure activity relationship studies of pyrimidine series as potent phosphodiesterase 10A inhibitors

Rostami

Fatemi

2018

J Chinese Chemical Soc

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The inhibition of phosphodiesterase (PDE) 10A enzyme as an alternative approach is shown to have potential in the current treatment of schizophrenia. In the present work, the inhibition activity of 87 structurally diverse pyrimidine derivatives as PDE10A inhibitors were subjected to 2-dimensional quantitative structure activity relationship (2D-QSAR) and 3D-QSAR studies using hologram QSAR and comparative molecular field analysis (CoMFA), respectively. The best CoMFA model gave a noncross-validated correlation coefficient, (r 2 ncv ) = 0.977, SE train = 0.247 and predictive correlation coefficient (r 2 pred ) = 0.957, and SE test = 0.358. For the hologram QSAR (HQSAR) model, the similar parameters were r 2 ncv = 0:889, SE train = 0.565, r 2 pred = 0:801, and SE test = 0.872. The leave-one-out (LOO) crossvalidated r 2 (q 2 ) were 0.78 and 0.8 for the CoMFA and HQSAR models, respectively. These statistics indicate that the CoMFA model is more satisfactory than the HQSAR. Analysis of CoMFA steric and electrostatic contour maps strongly demonstrate that the presence of bulky group at the end of R 1 substituent, especially heterocyclic aromatic rings, increase the inhibitory activities, and electronegative groups, such as alkoxide at the R 2 position, enhance the inhibitory activities of studied chemicals. The results of this study serves as a guide for the possible modifications and designing of novel pyrimidine derivatives as new potent PDE10A inhibitors.

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Molecular Descriptors in QSPR/QSAR Modeling

Ahmadi

Ketabi

Javan

2023

Challenges and Advances in Computational Chemistry and Physics

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A Theoretical Approach to Model and Predict the Adsorption Coefficients of Some Small Aromatic Molecules on Carbon Nanotube

Cited by 11 publications

References 16 publications

Prediction of some thermodynamic properties of sulfonamide drugs using genetic algorithm‐multiple linear regressions

Prediction of some thermodynamic properties of sulfonamide drugs using genetic algorithm‐multiple linear regressions

Comparative molecular field analysis and hologram quantitative structure activity relationship studies of pyrimidine series as potent phosphodiesterase 10A inhibitors

Molecular Descriptors in QSPR/QSAR Modeling

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