In-silico activity prediction, structure-based drug design, molecular docking and pharmacokinetic studies of selected quinazoline derivatives for their antiproliferative activity against triple negative breast cancer (MDA-MB231) cell line

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DC Field	Value	Language
dc.contributor.author	Abdullahi, Sagiru Hamza	vi
dc.contributor.other	Uzairu, Adamu	vi
dc.contributor.other	Shallangwa, Gideon Adamu	vi
dc.contributor.other	Uba, Sani	vi
dc.contributor.other	Umar, Abdullahi Bello	vi
dc.date.accessioned	2023-03-28T02:48:41Z	-
dc.date.available	2023-03-28T02:48:41Z	-
dc.date.issued	2022	-
dc.identifier.issn	2522-8307	vi
dc.identifier.uri	http://tailieuso.tlu.edu.vn/handle/DHTL/12599	-
dc.description.abstract	In this research work Genetic function algorithm was employed to generate the QSAR models due to its ability to produce a vast population of model instead of just a single model. Four models were generated from the model building set and the first one was chosen because of its statistical significance.	vi
dc.description.uri	https://link.springer.com/article/10.1186/s42269-021-00690-z	vi
dc.language	en_US	vi
dc.relation.ispartofseries	Bulletin of the National Research Centre, Volume 46 (2022), Article number: 2	vi
dc.subject	Density function theory	vi
dc.subject	Quantitative structure activity relationship	vi
dc.subject	Triple negative breast cancer	vi
dc.subject	Molecular docking	vi
dc.subject	Pharmacokinetic studies	vi
dc.title	In-silico activity prediction, structure-based drug design, molecular docking and pharmacokinetic studies of selected quinazoline derivatives for their antiproliferative activity against triple negative breast cancer (MDA-MB231) cell line	vi
dc.type	BB	vi
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