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dc.contributor.authorEjeh, Stephenvi
dc.contributor.otherUzairu, Adamuvi
dc.contributor.otherShallangwa, Gideon Adamuvi
dc.contributor.otherAbechi, Stephen E.vi
dc.date.accessioned2023-03-15T02:39:21Z-
dc.date.available2023-03-15T02:39:21Z-
dc.date.issued2021-
dc.identifier.issn2522-8307vi
dc.identifier.urihttp://tailieuso.tlu.edu.vn/handle/DHTL/12296-
dc.description.abstractThe model obtained by in-silico method have the following statistical records, coefficient of determination (r2) of 0.7704, cross-validation (q2LOO = 0.6914); external test set (r2(pred) = 0.7049) and Y-randomization assessment (cR2p = 0.7025). The results from the model were used to identify 12 new potential human HCV NS3/4A protease inhibitors, and it was observed that the identified molecule is well-fixed when docked with the receptor and was found to have the lowest binding energy of − 10.7, compared to approved direct-acting antiviral agents (Telaprevir, Simeprevir, and Voxilaprevir) with − 9.5, − 10.0, − 10.5 binding energy, respectively.vi
dc.description.urihttps://link.springer.com/article/10.1186/s42269-020-00467-wvi
dc.languageen_USvi
dc.relation.ispartofseriesBulletin of the National Research Centre, Volume 45 (2021), Article number: 3vi
dc.subjectComputer-aided drug designvi
dc.subjectQSARvi
dc.subjectHCV NS3/4A protease inhibitorsvi
dc.subjectMolecular dockingvi
dc.subjectMolecular descriptorsvi
dc.titleComputer-aided identification of a series of novel ligands showing high potency as hepatitis C virus NS3/4A protease inhibitorsvi
dc.typeBBvi
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