Inhibitors for the hepatitis C virus RNA polymerase explored by SAR with advanced machine learning methods
Links to Fileshttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3653294/
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Type of Work29 pages
Citation of Original PublicationWeidlich, 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.2013.03.032
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Attribution-NonCommercial-NoDerivs 3.0 United States
k Nearest Neighbor Simulated Annealing
Computational Drug Repositioning
High Performance Computing Facility (HPCF)
Hepatitis 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.
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