Dataset on in-silico investigation on triazole derivatives via molecular modelling approach: A potential glioblastoma inhibitors

In this work, ten molecular compounds were optimised using density functional theory (DFT) method via Spartan 14. The obtained descriptors were used to develop quantitative structural activities relationship (QSAR) model using Gretl and Matlab software and the similarity between predicted IC50 and o...

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Main Authors:	Abel Kolawole Oyebamiji, Oluwatumininu Abosede Mutiu, Folake Ayobami Amao, Olubukola Monisola Oyawoye, Temitope A Oyedepo, Babatunde Benjamin Adeleke, Banjo Semire
Format:	Article
Language:	English
Published:	Elsevier 2021-02-01
Series:	Data in Brief
Subjects:	Triazole Glioblastoma Inhibitors In-silico DFT QSAR
Online Access:	http://www.sciencedirect.com/science/article/pii/S2352340920315821

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spelling	doaj-d7135f46eeaf4377944c3d322d73c94b2021-01-06T04:05:34ZengElsevierData in Brief2352-34092021-02-0134106703Dataset on in-silico investigation on triazole derivatives via molecular modelling approach: A potential glioblastoma inhibitorsAbel Kolawole Oyebamiji0Oluwatumininu Abosede Mutiu1Folake Ayobami Amao2Olubukola Monisola Oyawoye3Temitope A Oyedepo4Babatunde Benjamin Adeleke5Banjo Semire6Department of Basic Sciences, Adeleke University, P.M.B. 250, Ede, Osun State, Nigeria; Computational Chemistry Laboratory, Department of Pure and Applied Chemistry, Ladoke Akintola University of Technology, P.M.B. 4000, Ogbomoso, Oyo State, Nigeria; Corresponding author.Department of Chemical Sciences, Osun State University, Osogbo, Osun State, NigeriaDepartment of Mathematics, Faculty of Science, Adeleke University, P.M.B. 250, Ede, Osun State, NigeriaDepartment of Microbiology, Laboratory of Molecular of Biology, Immunology and Bioinformatics, Adeleke University, P.M.B. 250, Ede, Osun State, NigeriaDepartment of Biochemistry, Adeleke University, P.M.B. 250, Ede, Osun State, NigeriaDepartment of Chemistry, University of Ibadan, Ibadan, Oyo State, NigeriaComputational Chemistry Laboratory, Department of Pure and Applied Chemistry, Ladoke Akintola University of Technology, P.M.B. 4000, Ogbomoso, Oyo State, NigeriaIn this work, ten molecular compounds were optimised using density functional theory (DFT) method via Spartan 14. The obtained descriptors were used to develop quantitative structural activities relationship (QSAR) model using Gretl and Matlab software and the similarity between predicted IC50 and observed IC50 was investigated. Also, docking study revealed the non-bonding interactions between the studied compounds and the receptor. The molecular interactions between the observed ligands and brain cancer protein (PDB ID: 1q7f) were investigated. Adsorption, distribution, metabolism, excretion and toxicity (ADMET) properties were also investigated.http://www.sciencedirect.com/science/article/pii/S2352340920315821TriazoleGlioblastomaInhibitorsIn-silicoDFTQSAR
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Abel Kolawole Oyebamiji Oluwatumininu Abosede Mutiu Folake Ayobami Amao Olubukola Monisola Oyawoye Temitope A Oyedepo Babatunde Benjamin Adeleke Banjo Semire
spellingShingle	Abel Kolawole Oyebamiji Oluwatumininu Abosede Mutiu Folake Ayobami Amao Olubukola Monisola Oyawoye Temitope A Oyedepo Babatunde Benjamin Adeleke Banjo Semire Dataset on in-silico investigation on triazole derivatives via molecular modelling approach: A potential glioblastoma inhibitors Data in Brief Triazole Glioblastoma Inhibitors In-silico DFT QSAR
author_facet	Abel Kolawole Oyebamiji Oluwatumininu Abosede Mutiu Folake Ayobami Amao Olubukola Monisola Oyawoye Temitope A Oyedepo Babatunde Benjamin Adeleke Banjo Semire
author_sort	Abel Kolawole Oyebamiji
title	Dataset on in-silico investigation on triazole derivatives via molecular modelling approach: A potential glioblastoma inhibitors
title_short	Dataset on in-silico investigation on triazole derivatives via molecular modelling approach: A potential glioblastoma inhibitors
title_full	Dataset on in-silico investigation on triazole derivatives via molecular modelling approach: A potential glioblastoma inhibitors
title_fullStr	Dataset on in-silico investigation on triazole derivatives via molecular modelling approach: A potential glioblastoma inhibitors
title_full_unstemmed	Dataset on in-silico investigation on triazole derivatives via molecular modelling approach: A potential glioblastoma inhibitors
title_sort	dataset on in-silico investigation on triazole derivatives via molecular modelling approach: a potential glioblastoma inhibitors
publisher	Elsevier
series	Data in Brief
issn	2352-3409
publishDate	2021-02-01
description	In this work, ten molecular compounds were optimised using density functional theory (DFT) method via Spartan 14. The obtained descriptors were used to develop quantitative structural activities relationship (QSAR) model using Gretl and Matlab software and the similarity between predicted IC50 and observed IC50 was investigated. Also, docking study revealed the non-bonding interactions between the studied compounds and the receptor. The molecular interactions between the observed ligands and brain cancer protein (PDB ID: 1q7f) were investigated. Adsorption, distribution, metabolism, excretion and toxicity (ADMET) properties were also investigated.
topic	Triazole Glioblastoma Inhibitors In-silico DFT QSAR
url	http://www.sciencedirect.com/science/article/pii/S2352340920315821
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Dataset on in-silico investigation on triazole derivatives via molecular modelling approach: A potential glioblastoma inhibitors

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