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 IC(50) and...

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Detalles Bibliográficos
Autores principales: Oyebamiji, Abel Kolawole, Mutiu, Oluwatumininu Abosede, Amao, Folake Ayobami, Oyawoye, Olubukola Monisola, Oyedepo, Temitope A, Adeleke, Babatunde Benjamin, Semire, Banjo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Data Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7797363/
https://www.ncbi.nlm.nih.gov/pubmed/33457478
http://dx.doi.org/10.1016/j.dib.2020.106703
Descripción
Sumario: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 IC(50) and observed IC(50) 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.

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