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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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
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author Oyebamiji, Abel Kolawole
Mutiu, Oluwatumininu Abosede
Amao, Folake Ayobami
Oyawoye, Olubukola Monisola
Oyedepo, Temitope A
Adeleke, Babatunde Benjamin
Semire, Banjo
author_facet Oyebamiji, Abel Kolawole
Mutiu, Oluwatumininu Abosede
Amao, Folake Ayobami
Oyawoye, Olubukola Monisola
Oyedepo, Temitope A
Adeleke, Babatunde Benjamin
Semire, Banjo
author_sort Oyebamiji, Abel Kolawole
collection PubMed
description 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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spelling pubmed-77973632021-01-15 Dataset on in-silico investigation on triazole derivatives via molecular modelling approach: A potential glioblastoma inhibitors Oyebamiji, Abel Kolawole Mutiu, Oluwatumininu Abosede Amao, Folake Ayobami Oyawoye, Olubukola Monisola Oyedepo, Temitope A Adeleke, Babatunde Benjamin Semire, Banjo Data Brief Data Article 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. Elsevier 2020-12-30 /pmc/articles/PMC7797363/ /pubmed/33457478 http://dx.doi.org/10.1016/j.dib.2020.106703 Text en © 2020 The Author(s) http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Oyebamiji, Abel Kolawole
Mutiu, Oluwatumininu Abosede
Amao, Folake Ayobami
Oyawoye, Olubukola Monisola
Oyedepo, Temitope A
Adeleke, Babatunde Benjamin
Semire, Banjo
Dataset on in-silico investigation on triazole derivatives via molecular modelling approach: A potential glioblastoma inhibitors
title Dataset on in-silico investigation on triazole derivatives via molecular modelling approach: A potential glioblastoma inhibitors
title_full Dataset on in-silico investigation on triazole derivatives via molecular modelling approach: A potential glioblastoma inhibitors
title_fullStr Dataset on in-silico investigation on triazole derivatives via molecular modelling approach: A potential glioblastoma inhibitors
title_full_unstemmed Dataset on in-silico investigation on triazole derivatives via molecular modelling approach: A potential glioblastoma inhibitors
title_short Dataset on in-silico investigation on triazole derivatives via molecular modelling approach: A potential glioblastoma inhibitors
title_sort dataset on in-silico investigation on triazole derivatives via molecular modelling approach: a potential glioblastoma inhibitors
topic Data Article
url 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
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