Theoretical Bioevaluation of 1,2,4-Thiadiazole-1,2,4-triazole Derivatives via Molecular Modelling Approach-Reference-Cited by-同舟云学术

Theoretical Bioevaluation of 1,2,4-Thiadiazole-1,2,4-triazole Derivatives via Molecular Modelling Approach

Published:2021 Issue:1 Volume:6 Page:40-46
ISSN:2456-8937
Container-title:Asian Journal of Organic & Medicinal Chemistry
language:en
Short-container-title:Asian J. Org. Med. Chem.

Author:

Kolawole Oyebamiji Abel¹^ORCID,Adewale Akintelu Sunday²^ORCID,Odoemene Simon N.³^ORCID,Emmanuel Oyeneyin Oluwatoba⁴,Banjo Semire⁵^ORCID

Affiliation:

1. 1Department of Basic Sciences, Adeleke University, P.M.B. 250, Ede, Osun State, Nigeria 2Computational Chemistry Research Laboratory, Department of Pure and Applied Chemistry, Ladoke Akintola University of Technology, P.M.B. 4000, Ogbomoso, Oyo State, Nigeria

2. School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing, P.R. China

3. Biological Science Research Laboratory, Department of Basic Sciences, Adeleke University, P.M.B. 250, Ede, Osun State, Nigeria

4. Theoretical and Computational Chemistry Unit, Department of Chemical Sciences, Adekunle Ajasin University, Akungba-Akoko, Ondo, State, Nigeria

5. Computational Chemistry Research Laboratory, Department of Pure and Applied Chemistry, Ladoke Akintola University of Technology, P.M.B. 4000, Ogbomoso, Oyo State, Nigeria

Abstract

Breast cancer still remains one of the precarious ailments among humans globally. The vulnerability of this ailment in homeopathic world remains colossal and this has drawn the attention of seasoned researchers to find lasting solution to this hazard. Therefore, 10 novel 1,2,4-thiadiazole-1,2,4-triazole derivatives were studied so as to explore their anti-breast cancer activities. The studied compounds were optimized using Spartan 14 and the QSAR study was executed by using Gretl and MATLAB. Also, docking study was observed using Pymol (for treating downloaded protein), Autodock Tool (for locating binding site in the downloaded protein and for converting ligand and receptor to .pdbqt format from .pdb format), Auto dock vina (for docking calculation) and discovery studio (for viewing the nonbonding interaction between the docked complexes). The selected descriptors were used to developed effective QSAR model and it was observed that the developed QSAR model using artificial neural network (ANN) predicted better than the prediction made by multiple linear regression (MLR). More so, the calculated binding affinity revealed that compound g (-11.4 kcal/mol) possess ability to inhibit 3α-hydroxysteroid dehydrogenase type 3 (PDB ID: 4xo6) than other studied compounds as well as etoposide (Standard).

Publisher

Asian Journal of Chemistry

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