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

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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Bibliographic Details
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
Description
Summary: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.
ISSN:2352-3409

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