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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Veröffentlicht in:Data in brief 2021-02, Vol.34, p.106703-106703, Article 106703
Hauptverfasser: Oyebamiji, Abel Kolawole, Mutiu, Oluwatumininu Abosede, Amao, Folake Ayobami, Oyawoye, Olubukola Monisola, Oyedepo, Temitope A, Adeleke, Babatunde Benjamin, Semire, Banjo
Format: Artikel
Sprache:eng
Schlagworte:
ADMET
Data
DFT
Docking
Glioblastoma
In-silico
Inhibitors
QSAR
Triazole
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Zusammenfassung: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
2352-3409
DOI:10.1016/j.dib.2020.106703