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dc.contributor.authorLinh, Tran Thi Thuy
dc.date.accessioned2013-07-17T04:00:31Z
dc.date.accessioned2018-05-29T08:22:03Z
dc.date.available2013-07-17T04:00:31Z
dc.date.available2018-05-29T08:22:03Z
dc.date.issued2011
dc.identifier.urihttp://10.8.20.7:8080/xmlui/handle/123456789/282
dc.description.abstractM2 proton channel of influenza A virus is the target protein of anti-flu drugs Amantadine and Rimantadine. However, these two powerful adamantane-based drugs lost their bioactivity because of mutations of virus in recent twenty years. There is a great urgent need to discover new promising M2 channel inhibitors based on computer-aided drug design methods. The structure of the M2 channel proteins of H3N2 (PDB code 2RLF) and 2009-H1N1 viruses (Genbank accession number GQ385383) may help researchers to solve the drug-resistant problem of these two adamantane-based drugs and develop more powerful new drugs against influenza A virus. Searching for new M2 channel inhibitors in this study was based on a combination of different computational methodologies, including attaching new functional groups to Amantadine scaffold, virtual screening with docking and pharmacophore modeling. Virtual screening was performed to calculate the free binding energies between receptor M2 channel proteins and 200 new designed ligands with the pharmacophore techniques to analyze the important M2 protein- inhibitor interactions and give the best common features of top binding compounds with M2 channel proteins. Finally, two most potential compounds were proposed as novel leads to inhibit M2 channel proteins in both H3N2 and 2009-H1N1 influenza A virus. Keywords: M2 channel inhibitors, adamantane-based drugs, Amantadine scaffold, virtual screening, pharmacophore analysis.en_US
dc.description.sponsorshipDr. Le Thi Lyen_US
dc.language.isoenen_US
dc.publisherInternational University HCMC, Vietnamen_US
dc.relation.ispartofseries;022000541
dc.subjectOrganic chemistryen_US
dc.titleComputer - Aided design of potent M2 channel inhibitorsen_US
dc.typeThesisen_US


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