Molecular docking and 4D-QSAR studies of metastatic cancer inhibitor thiazoles. (October 2018)

• Record Type: Journal Article
• Authors: Türkmenoğlu, Burçin; Güzel, Yahya
• Is Part Of: Computational biology and chemistry. Volume 76(2018)
• Page Start: 327
• Page End: 337
• Publication Date: 2018-10
• Subjects: Thiazole derivatives -- Molecular docking -- 4D-QSAR -- MCET; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2018.07.003
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: The MCET method was used as the 4D-QSAR method. 3D pharmacophore model have produced by the Klopman index. Based on the satisfactory LOO-CV method, r2 and q2 results were found. The study using docking has been compatible with many points of the 3D-PhaM given by MCET. Abstract: By using the molecular docking and 4D-QSAR analysis, it is aimed to find the interaction points in the receptor binding site of transforming growth factor-beta (TGF-beta) used to inhibit invasion and metastasis. To elucidate the interaction points of receptor, different types of local reactive descriptor (LRD) of ligands have been used. Activity values related to interaction energy between the ligand-receptor (L–R) were determined by nonlinear least squares (NLLS) using the Levenberg-Marquardt (LM) algorithm. Using the Molecule Comparative Electron Topology (MCET) method, the 3D pharmacophore model (3D-PhaM) was obtained after alignment and superimposition of the molecules, and also confirmed by molecular docking method. With the leave one out-cross validation (LOO-CV) method, the best predictions are q 2 or rCV 2 = 0.789 for the 51 compounds in the internal training set and r 2 = 0.785 for the 13 compounds in the external test set. Furthermore, the predictive capability of the advanced QSAR model is more precisely calculated with the rm 2 metric (rm 2 = 0.769).