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  1. Article ; Online: Molecular docking, molecular dynamics simulation, and ADMET analysis of levamisole derivatives against the SARS-CoV-2 main protease (MPro)

    Khalil EL Khatabi / Ilham Aanouz / Marwa Alaqarbeh / Mohammed Aziz Ajana / Tahar Lakhlifi / Mohammed Bouachrine

    BioImpacts, Vol 12, Iss 2, Pp 107-

    2022  Volume 113

    Abstract: Introduction: The new species of coronaviruses (CoVs), SARS-CoV-2, was reported as responsible for an outbreak of respiratory disease. Scientists and researchers are endeavoring to develop new approaches for the effective treatment against of the COVID- ... ...

    Abstract Introduction: The new species of coronaviruses (CoVs), SARS-CoV-2, was reported as responsible for an outbreak of respiratory disease. Scientists and researchers are endeavoring to develop new approaches for the effective treatment against of the COVID-19 disease. There are no finally targeted antiviral agents able to inhibit the SARS-CoV-2 at present. Therefore, it is of interest to investigate the potential uses of levamisole derivatives, which are reported to be antiviral agents targeting the influenza virus. Methods: In the present study, 12 selected levamisole derivatives containing imidazo[2,1-b]thiazole were subjected to molecular docking in order to explore the binding mechanisms between these derivatives and the SARS-CoV-2 Mpro (PDB: 7BQY). The levamisole derivatives were evaluated for in silico ADMET properties for wet-lab applicability. Further, the stability of the best-docked complex was checked using molecular dynamics (MD) simulation at 20 ns. Results: Levamisole derivatives and especially molecule N°6 showed more promising docking results, presenting favorable binding interactions as well as better docking energy compared to chloroquine and mefloquine. The results of ADMET prediction and MD simulation support the potential of the molecule N°6 to be further developed as a novel inhibitor able to stop the newly emerged SARS-CoV-2. Conclusion: This research provided an effective first line in the rapid discovery of drug leads against the novel CoV (SARS-CoV-2).
    Keywords covid-19 ; sars-cov-2 ; levamisole ; molecular docking ; molecular dynamics simulation ; in silico admet ; Medicine (General) ; R5-920 ; Biology (General) ; QH301-705.5
    Subject code 540
    Language English
    Publishing date 2022-02-01T00:00:00Z
    Publisher Tabriz University of Medical Sciences
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Catastrophic Collision Between Obesity and COVID-19 Have Evoked the Computational Chemistry for Research in Silico Design of New CaMKKII Inhibitors Against Obesity by Using 3D-QSAR, Molecular Docking, and ADMET

    Halima HAJJI / Fatima En-nahli / Ilham Aanouz / Hanane Zaki / Tahar Lakhlifi / Mohammed Aziz Ajana / Mohammed Bouachrine

    Orbital: The Electronic Journal of Chemistry, Vol 13, Iss 4, Pp 316-

    2021  Volume 327

    Abstract: The purpose of the paper is to discuss the various methods and computational approaches, which are used in computer-aided drug design. For this reason, pyrimidine and azaindole derivatives have been used to study the inhibitory activity of CaMKKII. It is ...

    Abstract The purpose of the paper is to discuss the various methods and computational approaches, which are used in computer-aided drug design. For this reason, pyrimidine and azaindole derivatives have been used to study the inhibitory activity of CaMKKII. It is an enzyme that enters the brain to greatly reduce food from regulating the production of Ghrelin that is synthesized by the stomach and acts on the hypothalamus. The obtained results from different techniques such as the 3D-QSAR, molecular docking, and ADMET were applied to study series of new CaMKKII inhibitors of 23 molecules based on pyrimidine and azaindole derivatives. The CoMFA and CoMSIA models were used in 19 molecules in the training set that give high values of determination coefficient R 2 0.970 and 0.902 respectively, and significant values of Leave-One-Out cross-validation coefficient Q 2 0.614 and 0.583 respectively. The predictive capacity of this model was examined by external validation though using a test set of four compounds with a predicted determination coefficient test R 2 ext of 0.778 and 0.972 successively. The method of alignment adapted with the appropriate parameters gave credible models. The CoMFA and CoMSIA models produce the contour maps which were used to define a 3D-QSAR mode. DOI: http://dx.doi.org/10.17807/orbital.v13i4.1608
    Keywords 3d-qsar ; admet ; camkkii inhibitors ; molecular docking ; obesity ; Science ; Q ; Chemistry ; QD1-999
    Subject code 540
    Language English
    Publishing date 2021-10-01T00:00:00Z
    Publisher Universidade Federal de Mato Grosso do Sul
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: 3D-QSAR and Molecular Docking Studies of p-Aminobenzoic Acid Derivatives to Explore the Features Requirements of Alzheimer Inhibitors

    Khalil El Khatabi / Ilham Aanouz / Reda El-mernissi / Ayoub Khaldan / Mohammed Aziz Ajana / Mohammed Bouachrine / Tahar Lakhlifi

    Orbital: The Electronic Journal of Chemistry, Vol 12, Iss 4, Pp 172-

    2020  Volume 181

    Abstract: In search of novel and more potent p-aminobenzoic acid derivatives previously evaluated as effective acetylcholinesterase inhibitors for the control of Alzheimer’s disease (AD), an integrated computational approach of three-dimensional quantitative ... ...

    Abstract In search of novel and more potent p-aminobenzoic acid derivatives previously evaluated as effective acetylcholinesterase inhibitors for the control of Alzheimer’s disease (AD), an integrated computational approach of three-dimensional quantitative structure–activity relationship and molecular docking were performed on a series of 20 compounds. The 3D-QSAR approach was applied to statistically study the structure-activity relationships (SAR) and had yielded good statistical significance for two high predictive models; comparative molecular field analysis (CoMFA: Q 2 =0.785; R 2 =0.936; rext 2 = 0.818) and comparative molecular similarity indices analysis (CoMSIA: Q 2 =0.831; R 2 =0.944; rext 2 = 0.931). Detailed analysis of the predictive models contour maps revealed that the hydrophobic and electrostatic fields govern the bioactivity and provided much helpful information to understand the features requirement in order to develop new potent acetylcholinesterase inhibitors. These findings were very useful for designing four novel inhibitors with enhanced activities targeting acetylcholinesterase. Through molecular docking, the newly designed compounds and compound 19 were docked on AChE as the protein target which helped to analyze the interaction characteristics and explore the binding modes at the active sites of the AChE. This work may be of utility for guiding the rational design of a new generation of acetylcholinesterase inhibitors. DOI: http://dx.doi.org/10.17807/orbital.v12i4.1467
    Keywords 3d-qsar ; acetylcholinesterase activity ; molecular docking ; molecular modeling ; p-aminobenzoic acid ; Science ; Q ; Chemistry ; QD1-999
    Subject code 540 ; 333
    Language English
    Publishing date 2020-12-01T00:00:00Z
    Publisher Universidade Federal de Mato Grosso do Sul
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: Design of Novel Benzimidazole Derivatives as Potential α-amylase Inhibitors by 3D-QSAR Modeling and Molecular Docking Studies

    Khalil EL KHATABİ / İlham AANOUZ / Reda EL-MERNİSSİ / Ayoub KHALDAN / Mohammed Aziz AJANA / Mohammed BOUACHRINE / Tahar LAKHLIFI

    Journal of the Turkish Chemical Society, Section A: Chemistry, Vol 7, Iss 2, Pp 471-

    2020  Volume 480

    Abstract: The α-amylase is an enzyme of a highly conserved glycoside hydrolase family, α-amylase inhibitors can be used as clinical agents for the treatment of Diabetes Mellitus (DM). A 3D-QSAR study was performed on 45 2-aryl benzimidazole derivatives, which have ...

    Abstract The α-amylase is an enzyme of a highly conserved glycoside hydrolase family, α-amylase inhibitors can be used as clinical agents for the treatment of Diabetes Mellitus (DM). A 3D-QSAR study was performed on 45 2-aryl benzimidazole derivatives, which have been identified as insulin-independent antidiabetic agents. The 3D-QSAR technique includes CoMFA with Q2 of 0.696 and R2 of 0.860 and CoMSIA with Q2 of 0.514 and R2 of 0.852. Both models were derived from a training set of 37 compounds based on an appropriate method of alignment, while the predictive ability was approved by a test set containing 8 compounds with rext2 values of 0.990 and 0.987, respectively. Moreover, contour maps generated from CoMFA and CoMSIA models provided much helpful information to figure out the features requirements that have control over the activity. To further reinforce the 3D-QSAR results, the molecular docking method was implemented which led to design new potent insulin-independent antidiabetic compounds with high predicted activity values.
    Keywords 3d-qsar ; molecular modeling ; computational study ; benzimidazole ; α-amylase inhibitors ; Chemistry ; QD1-999
    Subject code 540
    Language English
    Publishing date 2020-06-01T00:00:00Z
    Publisher Turkish Chemical Society
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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