Literature DB >> 35068344

3D-QSAR modeling and molecular docking studies on a series of 2, 4, 5-trisubstituted imidazole derivatives as CK2 inhibitors.

Amina Goudzal1, Abdellah El Aissouq1, Hicham El Hamdani2, El Ghalia Hadaji1, Abdelkrim Ouammou1, Mohammed Bouachrine3.   

Abstract

Protein case in kinase II alpha subunit (CK2) plays an imperative function in treating cancer disease. Herein, we have performed a three-dimensional quantitative structure activity relationship (3D-QSAR), and molecular docking analysis on a novel series of 2, 4, 5-trisubstituted imidazole derivatives in order to design potent kinase II alpha subunit (CK2) inhibitors. The 3D-QSAR methods such as comparative molecular similarity indexes analysis (COMSIA), and the comparative molecular field analysis (COMFA) were investigate using twenty-four molecules of 2, 4, 5-trisubstituted imidazole derivatives as anticancer agent. The best COMFA and COMSIA models exhibit excellent Q2 values of 0.66 and 0.75 and R2 values of 0.98 and 0.99 respectively. To check the validity of the selected COMFA and COMSIA models, a variety of validation tests were utilized: Internal validation analyses, and externally validation beside Y-randomization according to the principles of the Organization for Economic Co-operation and Development (OECD), and the Golbraikh and Tropsha's criteria for the validation of 3D-QSAR models. The proposed models for COMFA and COMSIA analysis have been successful. The developed models, indicating that they were reliable for activity prediction. Based on the preceding results, we designed several new potent molecules. Such outcome can proffer helpful theoretical references for future experimental studies.Communicated by Ramaswamy H. Sarma.

Entities:  

Keywords:  3D-QSAR; COMFA; COMSIA; docking analysis

Year:  2022        PMID: 35068344     DOI: 10.1080/07391102.2021.2014360

Source DB:  PubMed          Journal:  J Biomol Struct Dyn        ISSN: 0739-1102


  2 in total

1.  In-silico modelling studies of 5-benzyl-4-thiazolinone derivatives as influenza neuraminidase inhibitors via 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions.

Authors:  Mustapha Abdullahi; Adamu Uzairu; Gideon Adamu Shallangwa; Paul Andrew Mamza; Muhammad Tukur Ibrahim
Journal:  Heliyon       Date:  2022-08-08

2.  Computational modelling studies of some 1,3-thiazine derivatives as anti-influenza inhibitors targeting H1N1 neuraminidase via 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions.

Authors:  Mustapha Abdullahi; Adamu Uzairu; Gideon Adamu Shallangwa; Paul Andrew Mamza; Muhammad Tukur Ibrahim
Journal:  Beni Suef Univ J Basic Appl Sci       Date:  2022-08-19
  2 in total

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