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

ABSTRACT: 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.

SUBMITTER: Oyebamiji AK

PROVIDER: S-EPMC7797363 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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

Oyebamiji Abel Kolawole AK Mutiu Oluwatumininu Abosede OA Amao Folake Ayobami FA Oyawoye Olubukola Monisola OM Oyedepo Temitope A TA Adeleke Babatunde Benjamin BB Semire Banjo B

Data in brief 20201230

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 ob ...[more]

PMID: 33457478

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