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

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dc.contributor.authorOyebamiji AK
dc.contributor.authorMutiu OA
dc.contributor.authorAmao FA
dc.contributor.authorOyawoye OM
dc.contributor.authorOyedepo TA
dc.contributor.authorAdeleke BB
dc.contributor.authorSemire B
dc.date.accessioned2022-07-27T19:26:27Z
dc.date.available2022-07-27T19:26:27Z
dc.date.issued2021
dc.descriptionData in Brief
dc.description.abstractIn 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.
dc.identifier.citation10.1016/j.dib.2020.106703
dc.identifier.issn2352-3409
dc.identifier.urihttps://nerd.ethesis.ng/handle/123456789/399
dc.language.isoen
dc.subjectTriazole
dc.subjectGlioblastoma
dc.subjectInhibitors
dc.subjectDFT
dc.subjectQSAR
dc.subjectDocking
dc.subjectADMET
dc.titleDataset on in-silico investigation on triazole derivatives via molecular modelling approach: A potential glioblastoma inhibitors
dc.typeArticle
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