Quantitative structure - toxicity relationship studies of aromatic aldehydes to  Tetrahymena pyriformis  base d on electronic and topological  descriptors

Oussa, A.; Elidrissi, B.; Ghamali, Mounir; Chtita, Samir; Aouidate, Adnane; Bouachrine, Mohammed; Lakhlifi, Tahar

doi:10.26872/jmes.2018.9.1.29

Cited by 4 publications

(4 citation statements)

References 5 publications

(6 reference statements)

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“…A model without an applicability domain can presume the activity of all compounds, regardless of their features, compared to those counted in the aberrant training set. So the AD is a tool to detect compounds outside the applicability domain of the obtained QSAR model and the outliers in the training set [ 29 ].…”

Section: Methodsmentioning

confidence: 99%

Towards designing of a potential new HIV-1 protease inhibitor using QSAR study in combination with Molecular docking and Molecular dynamics simulations

et al. 2023

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Human Immunodeficiency Virus type 1 protease (HIV-1 PR) is one of the most challenging targets of antiretroviral therapy used in the treatment of AIDS-infected people. The performance of protease inhibitors (PIs) is limited by the development of protease mutations that can promote resistance to the treatment. The current study was carried out using statistics and bioinformatics tools. A series of thirty-three compounds with known enzymatic inhibitory activities against HIV-1 protease was used in this paper to build a mathematical model relating the structure to the biological activity. These compounds were designed by software; their descriptors were computed using various tools, such as Gaussian, Chem3D, ChemSketch and MarvinSketch. Computational methods generated the best model based on its statistical parameters. The model’s applicability domain (AD) was elaborated. Furthermore, one compound has been proposed as efficient against HIV-1 protease with comparable biological activity to the existing ones; this drug candidate was evaluated using ADMET properties and Lipinski’s rule. Molecular Docking performed on Wild Type, and Mutant Type HIV-1 proteases allowed the investigation of the interaction types displayed between the proteases and the ligands, Darunavir (DRV) and the new drug (ND). Molecular dynamics simulation was also used in order to investigate the complexes’ stability allowing a comparative study on the performance of both ligands (DRV & ND). Our study suggested that the new molecule showed comparable results to that of darunavir and maybe used for further experimental studies. Our study may also be used as pipeline to search and design new potential inhibitors of HIV-1 proteases.

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Section: Methodsmentioning

confidence: 99%

Towards designing of a potential new HIV-1 protease inhibitor using QSAR study in combination with Molecular docking and Molecular dynamics simulations

et al. 2023

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“…Table 4 shows the quantitative structure-activity relationship model developed from the descriptors obtained from 3D-strucutres of the investigated 1,2,4-thiadiazole-1,2,4-triazole derivatives via Dataset Division GUI 1.2 software [2 , 3] and Gretl software [4] . The statistical factors calculated for the developed QSAR model were squared correlation coefficient (R 2 ) (0.971), adjusted squared coefficient (Adj.R 2 ) (0.951), P-Value (≤0.001), F-Value (0.004), cross validation correlation coefficient (CVR 2 ) (0.972) and mean square error (0.4138).…”

Section: Data Descriptionmentioning

confidence: 99%

Dataset on theoretical bio-evaluation of 1,2,4-thiadiazole-1,2,4-triazole analogues against epidermal growth factor receptor kinase down regulating human lung cancer

et al. 2021

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Data from eight 1,2,4-thiadiazole-1,2,4-triazole derivatives were used to observe the anti-epidermal growth factor receptor kinase activities of 1,2,4-thiadiazole-1,2,4-triazole analogues thereby reducing human lung cancer. The software used to achieve this work were Spartan 14, Pymol, mgltools_win32_1.5.6, Auto dock vina and biovia2019.ds2019client. Also, the developed QSAR model was developed using the screened descriptors so as to inspect the closeness between the experimental IC 50 and the predicted IC 50 . More so, the binding affinity from 1,2,4-thiadiazole-1,2,4-triazole derivatives - epidermal growth factor receptor kinase complexes using molecular docking approach were reported. Also, the ADMET properties for selected compounds and proposed compounds with better binding affinity were reported.

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“…Table 3 showed the developed QSAR model using the calculated molecular descriptors using back propagation neural network (BPNN) via MATLAB software [ 4 , 5 ]. The developed QSAR model involved molecular weight, volume, polarisability, E HOMO and Log P. This set of descriptors were chosen because they best described anti- Staphylococcus aureus activities of compounds used in this work than other calculated descriptors.…”

Section: Data Descriptionmentioning

confidence: 99%

Dataset on Insilico approaches for 3,4-dihydropyrimidin-2(1H)-one urea derivatives as efficient Staphylococcus aureus inhibitor

2020

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Series of anti- Staphylococcus aureus were studied via quantum chemical method and several molecular descriptors were obtained which were further used to develop QSAR model using back propagation neural network method using MATLAB. More so, the molecular interaction observed between 3,4-dihydropyrimidin-2(1H)-one Urea Derivatives and Staphylococcus aureus Sortase (PDB ID Code: 2kid ) via docking was used as a screening tool for the studied compounds. The observed molecular compounds used in this work was also correlated to Lipinski rule of five and the developed QSAR model using selected descriptors from the optimized compounds was also examined for its predictability. Also, the observed molecular docking revealed the interaction between the studied complex.

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Quantitative structure - toxicity relationship studies of aromatic aldehydes to Tetrahymena pyriformis base d on electronic and topological descriptors

Cited by 4 publications

References 5 publications

Towards designing of a potential new HIV-1 protease inhibitor using QSAR study in combination with Molecular docking and Molecular dynamics simulations

Towards designing of a potential new HIV-1 protease inhibitor using QSAR study in combination with Molecular docking and Molecular dynamics simulations

Dataset on theoretical bio-evaluation of 1,2,4-thiadiazole-1,2,4-triazole analogues against epidermal growth factor receptor kinase down regulating human lung cancer

Dataset on Insilico approaches for 3,4-dihydropyrimidin-2(1H)-one urea derivatives as efficient Staphylococcus aureus inhibitor

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