Weidlich, Iwona E.Filippov, Igor V.Brown, JodianBasu, Neerja KaushikKrishnan, RamalingamNicklaus, Marc C.Thorpe, Ian F.2018-10-012018-10-012014-06-01Weidlich, 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.2013.03.032http://doi.org/10.1016/j.bmc.2013.03.032http://hdl.handle.net/11603/11425DOI:10.1016/j.bmc.2013.03.032 ; Author's post-print must be released with a Creative Commons Attribution Non-Commercial No Derivatives LicenseHepatitis 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.29 pagesen-USThis 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.Attribution-NonCommercial-NoDerivs 3.0 United StatesNS5BRdRpHCVSARHCV-796Random Forestk Nearest Neighbor Simulated AnnealingCandesartan cilexetilComputational Drug RepositioningUMBC High Performance Computing Facility (HPCF)Text