Molecular design and docking analysis of the inhibitory activities of some α_substituted acetamido-N-benzylacetamide as anticonvulsant agents

Abdulfatai, Usman; Shallangwa, Gideon Adamu; Umar, Bello Abdullahi; Ibrahim, Muhammad

doi:10.1007/s42452-019-0512-6

Cited by 14 publications

(10 citation statements)

References 16 publications

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“…The optimized structures of compounds with the best activity were saved in PDB format (Protein Data Bank), and then docked with the NMR structure of H5N1 virus M2 protein with PDB ID: 2KQT. The docking simulation was carried out using Auto Dock wizard of PyRx virtual screening tool [16,17]. Subsequently, the docking results were visualized using the Discovery Studio Visualizer v16.1.0.15350 to study the kind of interactions in the stable complex formed.…”

Section: In Silico Docking Studymentioning

confidence: 99%

In silico QSAR and molecular docking simulation of some novel aryl sulfonamide derivatives as inhibitors of H5N1 influenza A virus subtype

Abdullahi

Shallangwa

Uzairu

2020

Beni-Suef Univ J Basic Appl Sci

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Background: This research provides a comprehensive analysis of QSAR modeling performed on 25 aryl sulfonamide derivatives to predict their effective concentration (EC 50) against H5N1 influenza A virus by using some numerical information derived from structural and chemical features (descriptors) of the compounds to generate a statistically significant model. Subsequently, the molecular docking simulations were done so as to determine the binding modes of some potent ligands in the dataset with the M2 proton channel protein of the H5N1 influenza A virus as the target. Results: In building the QSAR model, the genetic algorithm task was employed in the variable selection of the descriptors which are used to form the multi-linear regression equation. The model with descriptors, RDF100m, nO, and RDF45p, showed satisfactory internal and external validation parameters (R 2 train = 0.72963, R 2 adjusted = 0.67169, Q 2 cv = 0.598, R 2 pred ¼ 0.67295, R 2 test = 0.6860) which passed the model criteria of acceptability. Docking simulation results of the more potent compounds (ligands 2, 3, and 8) revealed the formation of hydrophobic and hydrogen bonds with the binding pockets of M2 protein of influenza A virus. Conclusion: The results in this study can help to advance the research in designing (in silico design) and synthesis of more potent aryl sulfonamides derivatives against H5N1 influenza virus.

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Section: In Silico Docking Studymentioning

confidence: 99%

In silico QSAR and molecular docking simulation of some novel aryl sulfonamide derivatives as inhibitors of H5N1 influenza A virus subtype

Abdullahi

Shallangwa

Uzairu

2020

Beni-Suef Univ J Basic Appl Sci

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“…Before the docking analysis, ligands were prepared from the optimized structures in section 2.2 above and saved in pdb file format using Spartan'14 [1]. The 3D structure of EGFR enzyme was downloaded from the protein data bank (with pdb ID: 4zau).…”

Section: Molecular Dockingmentioning

confidence: 99%

“…Molecular docking on the EGFR enzyme and five most active 2, 3-dihydro- [1,4] dioxino [2, 3-f] quinazoline derivatives (ligands) (EGFR WT inhibitors) were studied ( Table 5). From Table 5, we can see that molecule 4 has the highest docking score of − 8.3 kcal/mol which might be as a result of hydrophobic interactions formed with LEU718, LEU792, LYS745, LEU788, VAL726, ALA743, and LEU844 amino acid residues in the active site of EGFR enzyme.…”

Section: Molecular Dockingmentioning

confidence: 99%

Computer-aided molecular modeling studies of some 2, 3-dihydro-[1, 4] dioxino [2, 3-f] quinazoline derivatives as EGFRWT inhibitors

Ibrahim

Uzairu

Shallangwa

2020

Beni-Suef Univ J Basic Appl Sci

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Background: Quinazoline are known to possess different biological activities which among is anti-cancer most especially NSCLC. Epidermal growth factor receptor (EGFR) belongs to the receptor tyrosine kinases (RTKs) family, which is known to be one of the most important therapeutic targets for the treatment of cancer most especially NSCLC. Results: QSAR modeling was performed to develop a model with high predictive power on some non-small cell lung cancer agents (NSCLC) (EGFR WT inhibitors). The EGFR WT inhibitors were optimized using density functional theory (DFT) method utilizing B3LYP/6-31G* level of theory. Genetic function algorithm (GFA) was used to build five models. Out of these five models, the studied one was selected and reported because of its fitness statistically with the following validation parameters: R 2 trng = 0.9459, R 2 adj = 0.9311, Q 2 cv = 0.8947, R 2 test = 0.7008, and LOF = 0.1195. The selected model was further subjected to other validation test such as VIF and Y-scrambling test applicability domain and found to be statistically significant. The kind of interactions between five most active EGFR WT inhibitors and EGFR WT enzyme were explored via molecular docking. Molecule 4 was ranked top in comparison to other ligands because it has the highest docking score of − 8.3 kcal/mol. The pharmacokinetics studies indicated that these molecules have good absorption, low toxicity level, and permeability properties because none of them violate the Lipinski's rule of five. Conclusion:A model with a very high predictive power on some EGFR WT inhibitors was developed using QSAR model. The model was validated and found to have good internal and external assessment parameters: R 2 of 0.9459, R 2 adj of 0.9311, Q cv 2 of 0.8947, R 2 test of 0.7008, and LOF of 0.1195. The nature of interaction of these molecules with their target protein was explored via molecular docking and found molecule 4 to have the highest docking score of − 8.3 kcal/mol among co-ligands. Pharmacokinetics studies revealed that these molecules have good absorption, low toxicity level, and permeability properties. These findings proposed a way for designing potent EGFR WT inhibitors against their target enzyme.

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“…Before the docking analysis, ligands were prepared from the optimized structures in 2.1.2 above saved in pdb file format using Spartan'14 [1]. The 3D crystal structure of human β-glucuronidase was downloaded from the protein data bank (with pdb ID 1bhg).…”

Section: Ligand and Receptor Preparationmentioning

confidence: 99%

Molecular Modelling, Docking and Pharmacokinetic Studies of N-Arylidenequinoline-3-Carbohydrazides Analogs as Novel β-Glucuronidase Inhibitors

et al. 2019

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Quantitative structure-activity relationships (QSAR) modelling on 30 N-Arylidenequinoline-3-carbohydrazides analogs was performed using Multi-Linear Regression (MLR) analysis adopting Genetic Function Algorithm (GFA) method. Semi empirical method using PM6 basis set was used for complete geometry optimization of the data set. The best model was chosen based on its statistical fit due to it good internal and external validations. From the Williams plot, it can be inferred that the reported model can make prediction of new compounds that are not within the data set. The molecular docking study showed that, the most active chemical in the data set was better than the standard β-glucuronidase inhibitor both in terms of binding scores and the amino acid residues that interacted with the drug and β-glucuronidase enzyme. The Pharmacokinetic studies indicated that none of the chemicals violated any of the condition set by the Lipinski′s Rule of five which confirm the bioavailability of these chemicals. The results these findings give room for designing novel β-glucuronidase inhibitors that are highly effective. Resumen. Se llevó a cabo la técnica de QSAR en 30 analogos de N-arilidenequinolina-3-carbohidrazidas mediante el analisis de regresesión lineal múltiple (MLS) adopatando el método del algoritmo de función genética (GFA). Para la optimización completa de la geometría del conjunto de datos se utilizó un método semiémpirico del conjunto de bases PM6. El mejor modelo fue elegido basado en función de su ajuste estadístico debido a su validación interna y externa. A partir de la gráfica de Williams, se puede inferir que el modelo reportado puede predecir nuevos compuestos que no se encuentran en el conjunto de datos. Este estudio de acomplamiento molecular mostró que, el químico más activo del conjunto de datos fue mejor que el inhibidor estándar β-glucuronidasa, tanto en términos de unión y en términos de interacción de los residuos con el fármaco y la enzima β-glucuronidasa. Los estudios farmacocinéticos que indicaron que ninguno de los fármacos incumple ninguna de las condiciones establecidas por la regla de cinco de Lipinski, en donde se confirma la biodisponibilidad de estos químicos. Los resultados de los hallazgos computacionales permiten diseñar nuevos inhibidores de la β-glucuronidasa que son altamente efectivos.

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Molecular design and docking analysis of the inhibitory activities of some α_substituted acetamido-N-benzylacetamide as anticonvulsant agents

Cited by 14 publications

References 16 publications

In silico QSAR and molecular docking simulation of some novel aryl sulfonamide derivatives as inhibitors of H5N1 influenza A virus subtype

In silico QSAR and molecular docking simulation of some novel aryl sulfonamide derivatives as inhibitors of H5N1 influenza A virus subtype

Computer-aided molecular modeling studies of some 2, 3-dihydro-[1, 4] dioxino [2, 3-f] quinazoline derivatives as EGFRWT inhibitors

Molecular Modelling, Docking and Pharmacokinetic Studies of N-Arylidenequinoline-3-Carbohydrazides Analogs as Novel β-Glucuronidase Inhibitors

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