In Silico Design of Novel Tetra-Substituted Pyridinylimidazoles Derivatives as c-Jun N-Terminal Kinase-3 Inhibitors, Using 2D/3D-QSAR Studies, Molecular Docking and ADMET Prediction

Aissouq, Abdellah El; Toufik, Hamid; Stitou, Mourad; Ouammou, Abdelkrim; Lamchouri, Fatima

doi:10.1007/s10989-019-09939-8

Cited by 18 publications

(9 citation statements)

References 39 publications

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“…According to the dimensions of molecular descriptors used for model generation, QSAR methods can be classified into several classes of modeling, such as 1D, 2D, 3D, 4D, 5D, and so forth [ 85 ]. In most cases, 2D- and 3D-QSAR studies are commonly used to evaluate the series of chemical compounds [ 86 , 87 , 88 ]. The 1D-QSAR approach allows for the determination of correlations for 1D descriptors (pKa, log P, structural fragments, and fingerprints) with biological activity [ 89 ].…”

Section: Category Of In Silico Modelsmentioning

confidence: 99%

Current Strategies in Assessment of Nanotoxicity: Alternatives to In Vivo Animal Testing

Huang

Lee

Hsu

et al. 2021

IJMS

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Millions of experimental animals are widely used in the assessment of toxicological or biological effects of manufactured nanomaterials in medical technology. However, the animal consciousness has increased and become an issue for debate in recent years. Currently, the principle of the 3Rs (i.e., reduction, refinement, and replacement) is applied to ensure the more ethical application of humane animal research. In order to avoid unethical procedures, the strategy of alternatives to animal testing has been employed to overcome the drawbacks of animal experiments. This article provides current alternative strategies to replace or reduce the use of experimental animals in the assessment of nanotoxicity. The currently available alternative methods include in vitro and in silico approaches, which can be used as cost-effective approaches to meet the principle of the 3Rs. These methods are regarded as non-animal approaches and have been implemented in many countries for scientific purposes. The in vitro experiments related to nanotoxicity assays involve cell culture testing and tissue engineering, while the in silico methods refer to prediction using molecular docking, molecular dynamics simulations, and quantitative structure–activity relationship (QSAR) modeling. The commonly used novel cell-based methods and computational approaches have the potential to help minimize the use of experimental animals for nanomaterial toxicity assessments.

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Section: Category Of In Silico Modelsmentioning

confidence: 99%

Current Strategies in Assessment of Nanotoxicity: Alternatives to In Vivo Animal Testing

Huang

Lee

Hsu

et al. 2021

IJMS

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“…Partial-least-squares (PLS) regression is a linear regression method [20,21], especially useful in the cases where the number of descriptors is greater than the number of observations (dataset compounds), and in the cases where the dataset compounds contain highly inter-correlated descriptors. Here, the PLS method was applied to generate the linear 2D-QSAR model, using the values of pIC50 as dependent variables (Y variables) and the significant selected descriptors as independent variables (X variables).…”

Section: Partial-least-squares (Pls) Analysismentioning

confidence: 99%

Molecular Modeling Studies of C-Glycosylfavone Derivatives as GSK-3β Inhibitors Based on QSAR and Docking Analysis

et al. 2021

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Glycogen synthase kinase-3 beta (GSK-3β) is implicated in abnormal hyperphosphorylation of the tau protein and its inhibitors may be a promising therapeutic approach for treating Alzheimer's disease. Here, a series of C-glycosylfavone derivatives as GSK-3β inhibitors was selected to perform two-dimensional quantitative structure activity relationship (2D-QSAR) method and docking analysis. The 2D-QSAR model was generated and validated using a dataset of 23 compounds and a test set of 5 compounds, respectively. The best model selected by the partial-least-squares (PLS) regression method revealed a regression coefficient (r 2 ) value of 0.85 and the mean-square-error (MSE) value of 0.04. The predictive ability and stability of the generated model was verified by external and internal validations, and gave the regression coefficient values of 0.93 and 0.72, respectively. Molecular docking analysis using AutoDock vina was carried out to explain the binding modes of C-glycosylfavone ligands with the GSK-3β receptor. Based on the obtained results, a novel series of C-glycosylfavone derivative was designed and their activity and binding affinity were predicted. The generated work could be helpful for the design and development of novel GSK-3β inhibitors.

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“…In order to judge the quality and goodness of the generated QSAR model, the leave-one-out (LOO) cross validation process is performed [40,41]. In this process, one compound is primarily eliminated from the training set.…”

Section: Leave-one-out Cross Validationmentioning

confidence: 99%

Computational investigation of unsaturated ketone derivatives as MAO-B inhibitors by using QSAR, ADME/Tox, molecular docking and molecular dynamics simulations

2021

Turk J Chem

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Computer-aided drug design (CADD) approaches have recently become essential in drug discovery. These techniques proved to be effective in various stages of the drug development process, reducing both the cost and time required to develop a drug compared to conventional methods [21]. In general, CADD approaches are classified into two types: structure-based drug design (SBDD) and ligand-based drug design (LBDD). When the 3D structure of the protein is

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In Silico Design of Novel Tetra-Substituted Pyridinylimidazoles Derivatives as c-Jun N-Terminal Kinase-3 Inhibitors, Using 2D/3D-QSAR Studies, Molecular Docking and ADMET Prediction

Cited by 18 publications

References 39 publications

Current Strategies in Assessment of Nanotoxicity: Alternatives to In Vivo Animal Testing

Current Strategies in Assessment of Nanotoxicity: Alternatives to In Vivo Animal Testing

Molecular Modeling Studies of C-Glycosylfavone Derivatives as GSK-3β Inhibitors Based on QSAR and Docking Analysis

Computational investigation of unsaturated ketone derivatives as MAO-B inhibitors by using QSAR, ADME/Tox, molecular docking and molecular dynamics simulations

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