Matches in SemOpenAlex for { <https://semopenalex.org/work/W4283726608> ?p ?o ?g. }
- W4283726608 endingPage "3438" @default.
- W4283726608 startingPage "3422" @default.
- W4283726608 abstract "Hepatitis C virus (HCV) infection causes viral hepatitis leading to hepatocellular carcinoma. Despite the clinical use of direct-acting antivirals (DAAs) still there is treatment failure in 5-10% cases. Therefore, it is crucial to develop new antivirals against HCV. In this endeavor, we developed the Anti-HCV platform using machine learning and quantitative structure-activity relationship (QSAR) approaches to predict repurposed drugs targeting HCV non-structural (NS) proteins. We retrieved experimentally validated small molecules from the ChEMBL database with bioactivity (IC50/EC50) against HCV NS3 (454), NS3/4A (495), NS5A (494) and NS5B (1671) proteins. These unique compounds were divided into training/testing and independent validation datasets. Relevant molecular descriptors and fingerprints were selected using a recursive feature elimination algorithm. Different machine learning techniques viz. support vector machine, k-nearest neighbour, artificial neural network, and random forest were used to develop the predictive models. We achieved Pearson's correlation coefficients from 0.80 to 0.92 during 10-fold cross validation and similar performance on independent datasets using the best developed models. The robustness and reliability of developed predictive models were also supported by applicability domain, chemical diversity and decoy datasets analyses. The Anti-HCV predictive models were used to identify potential repurposing drugs. Representative candidates were further validated by molecular docking which displayed high binding affinities. Hence, this study identified promising repurposed drugs viz. naftifine, butalbital (NS3), vinorelbine, epicriptine (NS3/4A), pipecuronium, trimethaphan (NS5A), olodaterol and vemurafenib (NS5B) etc. targeting HCV NS proteins. These potential repurposed drugs may prove useful in antiviral drug development against HCV." @default.
- W4283726608 created "2022-07-01" @default.
- W4283726608 creator A5007783921 @default.
- W4283726608 creator A5018842142 @default.
- W4283726608 creator A5044389615 @default.
- W4283726608 creator A5076194359 @default.
- W4283726608 creator A5083913641 @default.
- W4283726608 date "2022-01-01" @default.
- W4283726608 modified "2023-10-06" @default.
- W4283726608 title "Targeting non-structural proteins of Hepatitis C virus for predicting repurposed drugs using QSAR and machine learning approaches" @default.
- W4283726608 cites W1581048386 @default.
- W4283726608 cites W1734025422 @default.
- W4283726608 cites W1886572313 @default.
- W4283726608 cites W1972675532 @default.
- W4283726608 cites W1989831705 @default.
- W4283726608 cites W1990552018 @default.
- W4283726608 cites W1997514631 @default.
- W4283726608 cites W1998212728 @default.
- W4283726608 cites W2000307707 @default.
- W4283726608 cites W2015336471 @default.
- W4283726608 cites W2022132176 @default.
- W4283726608 cites W2027545702 @default.
- W4283726608 cites W2056708192 @default.
- W4283726608 cites W2057936693 @default.
- W4283726608 cites W2059132547 @default.
- W4283726608 cites W2072445394 @default.
- W4283726608 cites W2074466341 @default.
- W4283726608 cites W2081735355 @default.
- W4283726608 cites W2096541451 @default.
- W4283726608 cites W2097834518 @default.
- W4283726608 cites W2105668062 @default.
- W4283726608 cites W2105847002 @default.
- W4283726608 cites W2117505956 @default.
- W4283726608 cites W2129438079 @default.
- W4283726608 cites W2134967712 @default.
- W4283726608 cites W2136288530 @default.
- W4283726608 cites W2148864053 @default.
- W4283726608 cites W2159887157 @default.
- W4283726608 cites W2163237435 @default.
- W4283726608 cites W2169573102 @default.
- W4283726608 cites W2169678694 @default.
- W4283726608 cites W2216865272 @default.
- W4283726608 cites W2232785457 @default.
- W4283726608 cites W2285199248 @default.
- W4283726608 cites W2294881315 @default.
- W4283726608 cites W2342366514 @default.
- W4283726608 cites W2394500959 @default.
- W4283726608 cites W2489559155 @default.
- W4283726608 cites W2494944008 @default.
- W4283726608 cites W2610164930 @default.
- W4283726608 cites W2735196722 @default.
- W4283726608 cites W2767891136 @default.
- W4283726608 cites W2791243833 @default.
- W4283726608 cites W2809395091 @default.
- W4283726608 cites W2845328011 @default.
- W4283726608 cites W2891247326 @default.
- W4283726608 cites W2893056655 @default.
- W4283726608 cites W2904544378 @default.
- W4283726608 cites W2923305310 @default.
- W4283726608 cites W2939518231 @default.
- W4283726608 cites W2954901371 @default.
- W4283726608 cites W2987241345 @default.
- W4283726608 cites W3004770136 @default.
- W4283726608 cites W3023791128 @default.
- W4283726608 cites W3026166203 @default.
- W4283726608 cites W3087192953 @default.
- W4283726608 cites W3089233157 @default.
- W4283726608 cites W3094640617 @default.
- W4283726608 cites W3107473374 @default.
- W4283726608 cites W3118141639 @default.
- W4283726608 cites W3134463457 @default.
- W4283726608 cites W3145924029 @default.
- W4283726608 cites W3164954860 @default.
- W4283726608 cites W3165576806 @default.
- W4283726608 cites W3184192697 @default.
- W4283726608 cites W3197944718 @default.
- W4283726608 cites W3203215258 @default.
- W4283726608 cites W3207153748 @default.
- W4283726608 cites W4210252132 @default.
- W4283726608 cites W4210805091 @default.
- W4283726608 cites W4210815157 @default.
- W4283726608 cites W4212931218 @default.
- W4283726608 cites W4213112407 @default.
- W4283726608 cites W4226341751 @default.
- W4283726608 cites W2098850329 @default.
- W4283726608 doi "https://doi.org/10.1016/j.csbj.2022.06.060" @default.
- W4283726608 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35832613" @default.
- W4283726608 hasPublicationYear "2022" @default.
- W4283726608 type Work @default.
- W4283726608 citedByCount "4" @default.
- W4283726608 countsByYear W42837266082022 @default.
- W4283726608 countsByYear W42837266082023 @default.
- W4283726608 crossrefType "journal-article" @default.
- W4283726608 hasAuthorship W4283726608A5007783921 @default.
- W4283726608 hasAuthorship W4283726608A5018842142 @default.
- W4283726608 hasAuthorship W4283726608A5044389615 @default.
- W4283726608 hasAuthorship W4283726608A5076194359 @default.
- W4283726608 hasAuthorship W4283726608A5083913641 @default.