Quantitative structure–activity relationship studies of dibenzo[a,d]cycloalkenimine derivatives for non-competitive antagonists ofN-methyl-<scp>d</scp>-aspartate based on density functional theory with electronic and topological descriptors

Chtita, Samir; Larif, Majdouline; Ghamali, Mounir; Bouachrine, Mohammed; Lakhlifi, Tahar

doi:10.1016/j.jtusci.2014.10.006

Cited by 37 publications

(20 citation statements)

References 30 publications

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“…All the feedforward NN used in this paper are threelayer networks; the first (input) layer comprises six neurons, representing the pertinent descriptors obtained in MLR technique [31,32], whereas there are neither theoretical nor empirical rules to determine the number of quiet layers or the number of neuron layers; one hidden layer seems to be sufficient in the most chemical application of ANN. Some authors [33,34] have offered a parameter , leading to determining the number of hidden neurons, which plays a major role in determining the best ANN architecture. It is defined as follows: = (number of data points in the training set/sum of the number of connections in the NN).…”

Section: Discussionmentioning

confidence: 99%

Organic Compounds Based on (E)-N-Aryl-2-ethene-sulfonamide as Microtubule Targeted Agents in Prostate Cancer: QSAR Study

Hadaji

Bourass

Ouammou

et al. 2017

Advances in Physical Chemistry

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(E)-N-Aryl-2-ethene-sulfonamide and its derivatives are potent anticancer agents; these compounds inhibit cancer cells proliferation. A study of quantitative structure-activity relationship (QSAR) has been applied on 40 compounds based on (E)-N-Aryl-2-ethene-sulfonamide, in order to predict their anticancer biological activity. The principal components analysis is used for minimizing the base matrix and the multiple linear regression (MLR) and multiple nonlinear regression have been used to design the relationships between the molecular descriptor and anticancer properties of the sulfonamide derivatives. The validation of the models MLR and MNLR has been done by dividing the dataset into training and test set, the external validation of multiple correlation coefficients was RpIC50 = 0.81 for MLR and RpIC50 = 0.91 for MNLR. The artificial neural network (ANN) showed a correlation coefficient close to 0.96, which concluded that this latter model is more effective and much better than the other models. This obtained model (ANN) has been confirmed by two methods of LOO cross-validation and scrambling (or Y-randomization). The high correlation between experimental and predicted activity values was observed, indicating the validation and the good quality of the derived QSAR model.

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Section: Discussionmentioning

confidence: 99%

Organic Compounds Based on (E)-N-Aryl-2-ethene-sulfonamide as Microtubule Targeted Agents in Prostate Cancer: QSAR Study

Hadaji

Bourass

Ouammou

et al. 2017

Advances in Physical Chemistry

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“…The MLR and MNLR techniques were used to predict the association constant for drug binding to DNA values, Log (K). The equations were justified by the correlation coefficient (r), the Mean Squared Error (MSE), the Fishers F-statistic (F), and the significance level (Fvalue) [34].…”

Section: Discussionmentioning

confidence: 99%

QSPR studies of 9-aniliioacridine derivatives for their DNA drug binding properties based on density functional theory using statistical methods: Model, validation and influencing factors

Chtita

Hmamouchi

Larif

et al. 2016

Journal of Taibah University for Science

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Please cite this article as: S. Chtita, R. Hmamouchi, M. Larif, M. Ghamali, M. Bouachrine, T. Lakhlifi, QSPR studies of 9-aniliioacridine derivatives for their DNA drug binding properties based on density functional theory using statistical methods: Model, validation and influencing factors, Journal of Taibah University for Science (2015), http://dx.Abstract: As a continuation of our research on the development and optimization of the biological activities/proprieties of acridine derivatives, a series of 31 molecules based on 9-aniliioacridines (25 training set and 6 test set) were subjected to 3D quantitative structure propriety relationship QSPR analyses for their drug-DNA binding proprieties using multiple linear regression (MLR) and multiple non-linear regression (MNLR). Quantum chemical calculations using density functional theory (B3LYP/6-31G (d) DFT) methods was performed on the studied compounds and used to calculate the electronic and quantum chemical parameters. The models were used to predict the association constant of the DNA drug binding of the test set compounds, and the agreement between the experimental and predicted values was verified. The descriptors determined by QSPR studies were used for the study and design of new compounds. The statistical results indicate that the predicted values were in good agreement with the experimental results (r= 0.935 and r= 0.936 for MLR and MNLR, respectively). To validate the predictive power of the resulting models, the external validation multiple correlation coefficients were 0.932 and 0.939 for the MLR and the MNLR, respectively. These results show that both models possess a favourable estimation stability and good prediction power.

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“…Thus, for the model developed to predict the analgesic activity of a new molecule, its lever must be below the threshold value h* = 0.800. This molecule must belong to the domain of applicability of the model [74].…”

Section: Wherementioning

confidence: 99%

Qsar Approach to Estimating the Analgesic Activity of a Series of Tri-Substituted Pyrimidine Derivatives

Koné¹,

Traoré²,

Ouattara³

et al. 2019

SDRP-JCCMM

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Quantitative structure–activity relationship studies of dibenzo[a,d]cycloalkenimine derivatives for non-competitive antagonists ofN-methyl-d-aspartate based on density functional theory with electronic and topological descriptors

Cited by 37 publications

References 30 publications

Organic Compounds Based on (E)-N-Aryl-2-ethene-sulfonamide as Microtubule Targeted Agents in Prostate Cancer: QSAR Study

Organic Compounds Based on (E)-N-Aryl-2-ethene-sulfonamide as Microtubule Targeted Agents in Prostate Cancer: QSAR Study

QSPR studies of 9-aniliioacridine derivatives for their DNA drug binding properties based on density functional theory using statistical methods: Model, validation and influencing factors

Qsar Approach to Estimating the Analgesic Activity of a Series of Tri-Substituted Pyrimidine Derivatives

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