DFT-based QSAR studies and Molecular Docking of 1-Phenylcyclohexylamine Analogues as anticonvulsant of NMDA Receptor

The phencyclidine (PCP) and their analogues have been reported to exhibit inhibitory activities toward the N-methyl-D-aspartate receptor (NMDAR). To discover the QSAR between structure of PCP derivatives and Ki activities we have used density functional theory (DFT) to generate quantum descriptors, multiple regression linear (MLR) method was applied to establish QSAR model, and an artificial neural network (ANN), considering the relevant descriptors obtained with the MLR method is explored, a correlation coefficient of RANN = 0.912 was obtained with 6-4-1 ANN model. This model is tested by using a cross-validation method with the LOO procedure (RCV = 0.841). To study the configuration impact on activity, we proceed to the Molecular Docking of four configurations, two configurations of compound have (Ki = 502 nM) and two configurations of compound have (Ki = 1200nM). The phenyl group, when placed in an equatorial position in cis9e, a configuration of the less active compound, does not form π-sigma interaction. The superimposition of this configuration with trans7e reveals that the phenyl group of cis9e configuration is shifted from the binding site compared to trans7e which forms an interaction π-sigma throughout its phenyl group with ARG B: 894. So, we could claim that the cis9e is the configuration adopted by compound having (Ki = 502 nM).

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Prediction of the polarity parameter π for the radical derived from monomer

e-Polymers ◽

10.1515/epoly.2009.9.1.1562 ◽

2009 ◽

Vol 9 (1) ◽

Author(s):

Xinliang Yu ◽

Wenhao Yu ◽

Bing Yi ◽

Xueye Wang

Keyword(s):

Neural Network ◽

Quantum Chemical ◽

Density Functional ◽

Ann Model ◽

Quantum Chemical Descriptors ◽

Functional Theory ◽

The Neural Network ◽

Polarity Parameter ◽

Chemical Descriptors ◽

Artificial Neural Network Ann

AbstractAn artificial neural network (ANN) model was successfully developed for the modelling and prediction of the polarity parameter π used in the revised patterns scheme for the prediction of monomers reactivity ratios in radical polymerizations. Four quantum chemical descriptors based on density functional theory (DFT) calculations were used to develop the ANN model. The optimal condition of the neural network was obtained by adjusting various parameters by trial-and-error. Simulated with the final optimum BP neural network 4-4-1, the results show that the predicted parameter π values are in good agreement with the experimental ones, with the root mean square (rms) errors being 0.053 (R=0.960) for the training set and 0.070 (R=0.942) for the test set. The ANN model has better statistic quality than the MLR model, which indicates there are nonlinear relationships between these quantum chemical descriptors and the parameter π.

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QSAR Studies and Structure Property/Activity Relationships Applied in Pyrazine Derivatives as Antiproliferative Agents Against the BGC823

Acta Chimica Slovenica ◽

10.17344/acsi.2021.6875 ◽

2021 ◽

Vol 68 (4) ◽

pp. 882-895

Author(s):

Fatima Soualmia ◽

Salah Belaidi ◽

Noureddine Tchouar ◽

Touhami Lanez ◽

Samia Boudergua

Keyword(s):

Density Functional ◽

Dipole Moments ◽

Structure Property ◽

Functional Theory ◽

Qsar Studies ◽

Qsar Models ◽

Artificial Neural Network Ann ◽

Ab Initio Hf ◽

Similarity Method

Electronic structures, the effect of the substitution, structure physicochemical property/activity relationships and drug-likeness applied in pyrazine derivatives, have been studied at ab initio (HF, MP2) and B3LYP/DFT (density functional theory) levels. In the paper, the calculated values, i.e., NBO (natural bond orbitals) charges, bond lengths, dipole moments, electron affinities, heats of formation and quantitative structure-activity relationships (QSAR) properties are presented. For the QSAR studies, we used multiple linear regression (MLR) and artificial neural network (ANN) tatistical modeling. The results show a high correlation between experimental and predicted activity values, indicating the validation and the good quality of the derived QSAR models. In addition, statistical analysis reveals that the ANN technique with (9-4-1) architecture is more significant than the MLR model. The virtual screening based on the molecular similarity method and applicability domain of QSAR allowed the discovery of novel anti-proliferative activity candidates with improved activity.

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Molecular docking and molecular dynamics simulation studies to identify potent AURKA inhibitors: assessing the performance of density functional theory, MM-GBSA and mass action kinetics calculations

Journal of Biomolecular Structure and Dynamics ◽

10.1080/07391102.2019.1674695 ◽

2019 ◽

Vol 38 (14) ◽

pp. 4325-4335 ◽

Cited By ~ 6

Author(s):

Sathishkumar Chinnasamy ◽

Gurudeeban Selvaraj ◽

Aman Chandra Kaushik ◽

Satyavani Kaliamurthi ◽

Selvaraj Chandrabose ◽

...

Keyword(s):

Molecular Dynamics ◽

Density Functional Theory ◽

Molecular Docking ◽

Molecular Dynamics Simulation ◽

Density Functional ◽

Mass Action ◽

Dynamics Simulation ◽

Simulation Studies ◽

Functional Theory ◽

Mass Action Kinetics

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Density Functional Theory and Molecular Docking Investigations of the Chemical and Antibacterial Activities for 1-(4-Hydroxyphenyl)-3-phenylprop-2-en-1-one

Molecules ◽

10.3390/molecules26123631 ◽

2021 ◽

Vol 26 (12) ◽

pp. 3631

Author(s):

Ahmed M. Deghady ◽

Rageh K. Hussein ◽

Abdulrahman G. Alhamzani ◽

Abeer Mera

Keyword(s):

Density Functional Theory ◽

Antibacterial Activity ◽

Molecular Docking ◽

Carbonyl Group ◽

Active Sites ◽

Density Functional ◽

Chemical Reactivity ◽

Basis Set ◽

Molecular Docking Study ◽

Functional Theory

The present investigation informs a descriptive study of 1-(4-Hydroxyphenyl) -3-phenylprop-2-en-1-one compound, by using density functional theory at B3LYP method with 6-311G** basis set. The oxygen atoms and π-system revealed a high chemical reactivity for the title compound as electron donor spots and active sites for an electrophilic attack. Quantum chemical parameters such as hardness (η), softness (S), electronegativity (χ), and electrophilicity (ω) were yielded as descriptors for the molecule’s chemical behavior. The optimized molecular structure was obtained, and the experimental data were matched with geometrical analysis values describing the molecule’s stable structure. The computed FT-IR and Raman vibrational frequencies were in good agreement with those observed experimentally. In a molecular docking study, the inhibitory potential of the studied molecule was evaluated against the penicillin-binding proteins of Staphylococcus aureus bacteria. The carbonyl group in the molecule was shown to play a significant role in antibacterial activity, four bonds were formed by the carbonyl group with the key protein of the bacteria (three favorable hydrogen bonds plus one van der Waals bond) out of six interactions. The strong antibacterial activity was also indicated by the calculated high binding energy (−7.40 kcal/mol).

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Molecular docking and density functional theory studies on creatine, guanidinoacetic acid, and their phosphorylated analogues binding to muscle creatine kinase

Journal of Chemical Research ◽

10.1177/1747519820978583 ◽

2020 ◽

pp. 174751982097858

Author(s):

M Vraneš ◽

S Ostojić ◽

Č Podlipnik ◽

A Tot

Keyword(s):

Density Functional Theory ◽

Creatine Kinase ◽

Molecular Docking ◽

Density Functional ◽

Creatine Phosphate ◽

Docking Studies ◽

Muscle Creatine Kinase ◽

Functional Theory ◽

Guanidinoacetic Acid ◽

Muscle Creatine

Comparative molecular docking studies on creatine and guanidinoacetic acid, as well as their phosphorylated analogues, creatine phosphate, and phosphorylated guanidinoacetic acid, are investigated. Docking and density functional theory studies are carried out for muscle creatine kinase. The changes in the geometries of the ligands before and after binding to the enzyme are investigated to explain the better binding of guanidinoacetic acid and phosphorylated guanidinoacetic acid compared to creatine and creatine phosphate.

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