Inhibitors for the hepatitis C virus RNA polymerase explored by SAR with advanced machine learning methods

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dc.contributor.authorWeidlich, Iwona E.
dc.contributor.authorFilippov, Igor V.
dc.contributor.authorBrown, Jodian
dc.contributor.authorBasu, Neerja Kaushik
dc.contributor.authorKrishnan, Ramalingam
dc.contributor.authorNicklaus, Marc C.
dc.contributor.authorThorpe, Ian F.
dc.date.accessioned2018-10-01T14:07:00Z
dc.date.available2018-10-01T14:07:00Z
dc.date.issued2014-06-01
dc.descriptionDOI:10.1016/j.bmc.2013.03.032 ; Author's post-print must be released with a Creative Commons Attribution Non-Commercial No Derivatives Licenseen_US
dc.description.abstractHepatitis C virus (HCV) is a global health challenge, affecting approximately 200 million people worldwide. In this study we developed SAR models with advanced machine learning classifiers Random Forest and k Nearest Neighbor Simulated Annealing for 679 small molecules with measured inhibition activity for NS5B genotype 1b. The activity was expressed as a binary value (active/inactive), where actives were considered molecules with IC50 ≤ 0.95 μM. We applied our SAR models to various drug-like databases and identified novel chemical scaffolds for NS5B inhibitors. Subsequent in vitro antiviral assays suggested a new activity for an existing prodrug, Candesartan cilexetil, which is currently used to treat hypertension and heart failure but has not been previously tested for anti-HCV activity. We also identified NS5B inhibitors with two novel non-nucleoside chemical motifs.en_US
dc.description.sponsorshipThis project has been funded in part with federal funds from the National Cancer Institute, National Institutes of Health, under contract N01-CO-12400. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government. This Research was supported in part by the Intramural Research Program of the NIH, National Cancer Institute, Center for Cancer Research. This project has been funded in part with Federal Funds from the Division of Microbiology and Infectious Diseases, National Institute of Allergy and Infectious Diseases, National Institute of Health, Department of Health and Human Services, under contract HHSN272201100012I. HCV NS5B inhibition studies were supported by the National Institute of Health research grant CA153147 to N.K-B.en_US
dc.description.urihttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3653294/en_US
dc.format.extent29 pagesen_US
dc.genrejournal articleen_US
dc.identifierdoi:10.13016/M25Q4RQ5W
dc.identifier.citationWeidlich, I. E., Filippov, I. V., Brown, J., Kaushik-Basu, N., Krishnan, R., Nicklaus, M. C., & Thorpe, I. F. (2013). Inhibitors for the hepatitis C virus RNA polymerase explored by SAR with advanced machine learning methods. Bioorganic & Medicinal Chemistry, 21(11), 3127–3137. http://doi.org/10.1016/j.bmc.2013.03.032en_US
dc.identifier.urihttp://doi.org/10.1016/j.bmc.2013.03.032
dc.identifier.urihttp://hdl.handle.net/11603/11425
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Chemistry & Biochemistry Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectNS5Ben_US
dc.subjectRdRpen_US
dc.subjectHCVen_US
dc.subjectSARen_US
dc.subjectHCV-796en_US
dc.subjectRandom Foresten_US
dc.subjectk Nearest Neighbor Simulated Annealingen_US
dc.subjectCandesartan cilexetilen_US
dc.subjectComputational Drug Repositioningen_US
dc.subjectUMBC High Performance Computing Facility (HPCF)en_US
dc.titleInhibitors for the hepatitis C virus RNA polymerase explored by SAR with advanced machine learning methodsen_US
dc.typeTexten_US

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