Hologram QSAR study on the electrophoretic mobility of aromatic acids

Jiao, Long; Xiao-feng, Zhang; Qin, Yucui; Li, Hua

doi:10.1016/j.chemolab.2016.06.020

Cited by 13 publications

(5 citation statements)

References 35 publications

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“…The backward stepwise multiple linear regression (BW-MLR) model was obtained by the SPSS statistics [43][44][45] version 22, and also the BP-ANN method is presented with Neural Network Toolbox techniques in MATLAB R2017b [46] were employed to determine the relationship between the pIC 50 and molecular descriptors of the 2-phenylindole derivatives [47,48].…”

Section: Methodsmentioning

confidence: 99%

Comparative QSAR modeling of 2‐phenylindol derivatives for predicting the anticancer activity using genetic algorithm multiple linear regression and back‐propagation‐artificial neural network techniques

Bahrami

Shafiei

Marjani

et al. 2022

Int J of Quantum Chemistry

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Quantitative structure-activity relationship (QSAR) studies on a series of 2-phenylindole derivatives as anticancer drugs were performed to choice the important descriptor, which is responsible for their anticancer activity (expressed as pIC 50 ). The geometry optimizations were performed on the structures using Gaussian software with the density functional B3LYP and 6-311G(d,p) basis sets.Dragon software was used to calculate molecular descriptors, and the genetic algorithm (GA) procedure and backward regression were used to proper selection of the most relevant descriptors. The backward multiple linear regression (BW-MLR) and backpropagation-artificial artificial neural network (BP-ANN) were carried out to design QSAR models. The squared correlation coefficient (R 2 ) and the root mean squared error (RMSE) values of the GA-MLR model were calculated to be 0.2843 and 0.7001, respectively. The BP-ANN model was the most powerful, with the square of predictive correlation coefficient R 2 pred , root mean square error (RMSE), and absolute average deviation (AAD) which was equal to 0.9416, 0.0238, and 0.0099, respectively.

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Section: Methodsmentioning

confidence: 99%

Comparative QSAR modeling of 2‐phenylindol derivatives for predicting the anticancer activity using genetic algorithm multiple linear regression and back‐propagation‐artificial neural network techniques

Bahrami

Shafiei

Marjani

et al. 2022

Int J of Quantum Chemistry

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“…The MLR method based on backward stepwise using the SPSS software (Version 21), and also the BP-ANN method using the MATLAB were employed to obtain a suitable QSTR model. [26,27] software. [28,29] The studied 76 molecules were optimized by Gaussian 09W program, and used as input data to the DRAGON program and 3,224 molecular descriptors were extracted.…”

Section: Methodsmentioning

confidence: 99%

“…The structure of the compounds was drawn by the Gauss view software and then optimized using Gaussian 09W program on the DFT‐B3LYP method and a 6‐31G* basis set for calculating the molecular descriptors. The MLR method based on backward stepwise using the SPSS software (Version 21), and also the BP‐ANN method using the MATLAB were employed to obtain a suitable QSTR model …”

Section: Methodsmentioning

confidence: 99%

Comparison between genetic algorithm‐multiple linear regression and back‐propagation‐artificial neural network methods for predicting the LD₅₀ of organo (phosphate and thiophosphate) compounds

Kianpour

Mohammadinasab

Isfahani

2020

J Chinese Chemical Soc

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The DFT-B3LYP functional method on the 6-31G* basis set was employed to optimize and calculate molecular descriptors of 76 organo (phosphates and thiophosphate) derivatives. The molecular descriptors were used to establish the quantitative structure-toxicity relationship (QSTR) for the acute oral toxicity of studied compounds by multiple linear regression (MLR) and artificial neural network (ANN) methods. The best results were obtained with an ANN model trained with the back-propagation (BP-ANN) algorithm. The prediction accuracy for the external test set was estimated by the root mean square (RMS) error, square correlation coefficient (R 2 ), and absolute average deviation (AAD) which were equal to 0.0248797, 0.9091, and 15.12187, respectively. It was specified that 90.91% of external test set was correctly predicted and the present model proved to be superior to the MLR model. Accordingly, the model developed in this study can be used to predict the oral acute toxicity of organo (phosphates and thiophosphate) derivatives, particularly for those that have not been experienced as well as new compounds. The analysis of the statistical parameters of models published in the literature showed that our model was more efficient.

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“…It relies on 2D chemical database storage and the linear notation that defines the chemical structure, a process which involves the generation of fragments hash into an array called molecular holograms [22,23]. The ability of molecular alignment and conformational specification is achieved by converting the representation of the chemical structure of a molecule into the corresponding molecular hologram [24,25]. According to the principle of the holograms phase diagram, the effect of any group or atom in the drug molecule on the drug activity can be precisely determined, and the operation process is relatively fast [26].…”

Section: Hqsarmentioning

confidence: 99%

QSAR Study, Molecular Docking and Molecular Dynamic Simulation of Aurora Kinase Inhibitors Derived from Imidazo[4,5-b]pyridine Derivatives

Tian,

Tong,

Liu

et al. 2024

Molecules

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Cancer is a serious threat to human life and social development and the use of scientific methods for cancer prevention and control is necessary. In this study, HQSAR, CoMFA, CoMSIA and TopomerCoMFA methods are used to establish models of 65 imidazo[4,5-b]pyridine derivatives to explore the quantitative structure-activity relationship between their anticancer activities and molecular conformations. The results show that the cross-validation coefficients q2 of HQSAR, CoMFA, CoMSIA and TopomerCoMFA are 0.892, 0.866, 0.877 and 0.905, respectively. The non-cross-validation coefficients r2 are 0.948, 0.983, 0.995 and 0.971, respectively. The externally validated complex correlation coefficients r2pred of external validation are 0.814, 0.829, 0.758 and 0.855, respectively. The PLS analysis verifies that the QSAR models have the highest prediction ability and stability. Based on these statistics, virtual screening based on R group is performed using the ZINC database by the Topomer search technology. Finally, 10 new compounds with higher activity are designed with the screened new fragments. In order to explore the binding modes and targets between ligands and protein receptors, these newly designed compounds are conjugated with macromolecular protein (PDB ID: 1MQ4) by molecular docking technology. Furthermore, to study the nature of the newly designed compound in dynamic states and the stability of the protein-ligand complex, molecular dynamics simulation is carried out for N3, N4, N5 and N7 docked with 1MQ4 protease structure for 50 ns. A free energy landscape is computed to search for the most stable conformation. These results prove the efficient and stability of the newly designed compounds. Finally, ADMET is used to predict the pharmacology and toxicity of the 10 designed drug molecules.

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Hologram QSAR study on the electrophoretic mobility of aromatic acids

Cited by 13 publications

References 35 publications

Comparative QSAR modeling of 2‐phenylindol derivatives for predicting the anticancer activity using genetic algorithm multiple linear regression and back‐propagation‐artificial neural network techniques

Comparative QSAR modeling of 2‐phenylindol derivatives for predicting the anticancer activity using genetic algorithm multiple linear regression and back‐propagation‐artificial neural network techniques

Comparison between genetic algorithm‐multiple linear regression and back‐propagation‐artificial neural network methods for predicting the LD₅₀ of organo (phosphate and thiophosphate) compounds

QSAR Study, Molecular Docking and Molecular Dynamic Simulation of Aurora Kinase Inhibitors Derived from Imidazo[4,5-b]pyridine Derivatives

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Hologram QSAR study on the electrophoretic mobility of aromatic acids

Cited by 13 publications

References 35 publications

Comparative QSAR modeling of 2‐phenylindol derivatives for predicting the anticancer activity using genetic algorithm multiple linear regression and back‐propagation‐artificial neural network techniques

Comparative QSAR modeling of 2‐phenylindol derivatives for predicting the anticancer activity using genetic algorithm multiple linear regression and back‐propagation‐artificial neural network techniques

Comparison between genetic algorithm‐multiple linear regression and back‐propagation‐artificial neural network methods for predicting the LD50 of organo (phosphate and thiophosphate) compounds

QSAR Study, Molecular Docking and Molecular Dynamic Simulation of Aurora Kinase Inhibitors Derived from Imidazo[4,5-b]pyridine Derivatives

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Comparison between genetic algorithm‐multiple linear regression and back‐propagation‐artificial neural network methods for predicting the LD₅₀ of organo (phosphate and thiophosphate) compounds