In silico design of potential HCV NS5B inhibitors: a comprehensive approach combining combinatorial library generation, ensemble docking, MM-GBSA calculations, QSAR model development, and molecular dynamics

Year 2025, Volume: 29 Issue: 2, 871 - 891

Berin Karaman Mayack , Muhammed Moyasar Alayoubi , Hakan Mikail Gezginci

https://doi.org/10.12991/jrespharm.1634330

Abstract

HCV is a blood-borne RNA virus that causes acute and chronic hepatitis, cirrhosis, liver failure, and hepatocellular carcinoma. In the present work, a large in silico combinatorial library was generated using the privileged substructures of existing inhibitors of the HCV NS5B protein. Next, we performed a multistep virtual screening process to identify novel HCV NS5B inhibitors. Additionally, we assessed the hit compounds' pharmacokinetic characteristics to evaluate their potential as drugs. Hit molecules with drug-like properties were classified with fingerprint-based chemical similarity clustering. Molecular dynamics simulations confirmed the stability of complexes and provided a comprehensive understanding of the molecular interactions between the novel molecule classes and HCV NS5B polymerase. The results of this study set the stage for developing new scaffolds as allosteric inhibitors of HCV NS5B protein for drug designing objectives and highlight the promising prospects of using privileged substructures for screening library construction in pharmaceutical research.

Keywords

NS5B, Docking, MM-GBSA, QSAR, ADME, Molecular Dynamics

Ethical Statement

Ethical approval is not required.

Supporting Institution

The Scientific and Technological Research Council of Türkiye (Project Number: 1109B321801603).

Project Number

1109B321801603

Thanks

Authors would like to thank E. D. Dincel and O. Soylu Eter (Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Istanbul University, 34134, Istanbul, Turkey) for their data collection support.

References

• [1] Li HC, Lo SY. Hepatitis C virus: Virology, diagnosis and treatment. World J Hepatol. 2015;7(10):1377-1389. https://doi.org/10.4254/wjh.v7.i10.1377.
• [2] Zein NN. Clinical significance of hepatitis C virus genotypes. Clin Microbiol Rev. 2000;13(2):223-235. https://doi.org/10.1128/cmr.13.2.223.
• [3] Morozov VA, Lagaye S. Hepatitis C virus: Morphogenesis, infection and therapy. World J Hepatol. 2018;10(2):186-212. https://doi.org/10.4254/wjh.v10.i2.186.
• [4] Pimenov N, Kostyushev D, Komarova S, Fomicheva A, Urtikov A, Belaia O, Umbetova K, Darvina O, Tsapkova N, Chulanov V. Epidemiology and Genotype Distribution of Hepatitis C Virus in Russia. Pathogens. 2022;11(12):1482. https://doi.org/10.3390/pathogens11121482.
• [5] Yang J, Qi JL, Wang XX, Li XH, Jin R, Liu BY, Liu HX, Rao HY. The burden of hepatitis C virus in the world, China, India, and the United States from 1990 to 2019. Front Public Health. 2023;11:1041201. https://doi.org/10.3389/fpubh.2023.1041201.
• [6] Gu M, Rice CM. Structures of hepatitis C virus nonstructural proteins required for replicase assembly and function. Curr Opin Virol. 2013;3(2):129-136. https://doi.org/10.1016/j.coviro.2013.03.013.
• [7] Moradpour D, Penin F. Hepatitis C virus proteins: from structure to function. Curr Top Microbiol Immunol. 2013;369:113-142. https://doi.org/10.1007/978-3-642-27340-7_5.
• [8] Kumar A, Narang RK, Bhatia R. Recent advancements in NS5B inhibitors (2011-2021): Structural insights, SAR studies and clinical status. J Mol Struct. 2023;1293:136272. https://doi.org/10.1016/j.molstruc.2023.136272.
• [9] Ganta NM, Gedda G, Rathnakar B, Satyanarayana M, Yamajala B, Ahsan MJ, Jadav SS, Balaraju T. A review on HCV inhibitors: Significance of non-structural polyproteins. Eur J Med Chem. 2019;164:576-601. https://doi.org/10.1016/j.ejmech.2018.12.045.
• [10] Membreno FE, Lawitz EJ. The HCV NS5B nucleoside and non-nucleoside inhibitors. Clin Liver Dis. 2011;15(3):611-626. https://doi.org/10.1016/j.cld.2011.05.003.
• [11] Abdurakhmanov E, Øie Solbak S, Danielson UH. Biophysical Mode-of-Action and Selectivity Analysis of Allosteric Inhibitors of Hepatitis C Virus (HCV) Polymerase. Viruses. 2017;9(6):151. https://doi.org/10.3390/v9060151.
• [12] Trivella JP, Gutierrez J, Martin P. Dasabuvir : a new direct antiviral agent for the treatment of hepatitis C. Expert Opin Pharmacother. 2015;16(4):617-624. https://doi.org/10.1517/14656566.2015.1012493.
• [13] Poordad F, Sedghi S, Pockros PJ, Ravendhran N, Reindollar R, Lucey MR, Epstein M, Bank L, Bernstein D, Trinh R, Krishnan P, Polepally AR, Unnebrink K, Martinez M, Nelson DR. Efficacy and safety of ombitasvir/paritaprevir/ritonavir and dasabuvir with low-dose ribavirin in patients with chronic hepatitis C virus genotype 1a infection without cirrhosis. J Viral Hepat. 2019;26(8):1027-1030. https://doi.org/10.1111/jvh.13109.
• [14] El Kassas M, Elbaz T, Hafez E, Wifi MN, Esmat G. Discovery and preclinical development of dasabuvir for the treatment of hepatitis C infection. Expert Opin Drug Discov. 2017;12(6):635-642. https://doi.org/10.1080/17460441.2017.1322955.
• [15] Mantry PS, Pathak L. Dasabuvir (ABT333) for the treatment of chronic HCV genotype I: a new face of cure, an expert review. Expert Rev Anti Infect Ther. 2016;14(2):157-165. https://doi.org/10.1586/14787210.2016.1120668.
• [16] Gentles RG. Discovery of Beclabuvir: A Potent Allosteric Inhibitor of the Hepatitis C Virus Polymerase. HCV: The Journey from Discovery to a Cure. 2019;31:193-228. https://doi.org/10.1007/7355_2018_38.
• [17] Bernal LA, Soti V. Hepatitis C Virus: Insights Into Its History, Treatment, Challenges, and Future Directions. Cureus. 2023;15(8):e43924. https://doi.org/10.7759/cureus.43924.
• [18] Parlati L, Hollande C, Pol S. Treatment of hepatitis C virus infection. Clin Res Hepatol Gastroenterol. 2021;45(4):101578. https://doi.org/10.1016/j.clinre.2020.11.008.
• [19] Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE. The Protein Data Bank. Nucleic Acids Res. 2000;28(1):235-242. https://doi.org/10.1093/nar/28.1.235.
• [20] Chen YC. Beware of docking! Trends Pharmacol Sci. 2015;36(2):78-95. https://doi.org/10.1016/j.tips.2014.12.001.
• [21] Yuriev E, Agostino M, Ramsland PA. Challenges and advances in computational docking: 2009 in review. J Mol Recognit. 2011;24(2):149-164. https://doi.org/10.1002/jmr.1077.
• [22] Durrant JD, McCammon JA. Molecular dynamics simulations and drug discovery. BMC Biol. 2011;9:71. https://doi.org/10.1186/1741-7007-9-71.
• [23] Amaro RE, Baudry J, Chodera J, Demir Ö, McCammon JA, Miao Y, Smith JC. Ensemble Docking in Drug Discovery. Biophys J. 2018;114(10):2271-2278. https://doi.org/10.1016/j.bpj.2018.02.038.
• [24] Greenidge PA, Kramer C, Mozziconacci JC, Sherman W. Improving docking results via reranking of ensembles of ligand poses in multiple X-ray protein conformations with MM-GBSA. J Chem Inf Model. 2014;54(10):2697-2717. https://doi.org/10.1021/ci5003735.
• [25] Slynko I, Scharfe M, Rumpf T, Eib J, Metzger E, Schüle R, Jung M, Sippl W. Virtual Screening of PRK1 Inhibitors: Ensemble Docking, Rescoring Using Binding Free Energy Calculation and QSAR Model Development. J Chem Inf Model. 2014;54(1):138-150. https://doi.org/10.1021/ci400628q.
• [26] Amaro RE, Li WW. Emerging methods for ensemble-based virtual screening. Curr Top Med Chem. 2010;10(1):3-13. https://doi.org/10.2174/156802610790232279.,
• [27] Bolcato G, Cuzzolin A, Bissaro M, Moro S, Sturlese M. Can We Still Trust Docking Results? An Extension of the Applicability of DockBench on PDBbind Database. Int J Mol Sci. 2019;20(14):3558. https://doi.org/10.3390/ijms20143558.
• [28] Plewczynski D, Łaźniewski M, Augustyniak R, Ginalski K. Can we trust docking results? Evaluation of seven commonly used programs on PDBbind database. J Comput Chem. 2011;32(4):742-755. https://doi.org/10.1002/jcc.21643.
• [29] Warren GL, Andrews CW, Capelli AM, Clarke B, LaLonde J, Lambert MH, Lindvall M, Nevins N, Semus SF, Senger S, Tedesco G, Wall ID, Woolven JM, Peishoff CE, Head MS. A critical assessment of docking programs and scoring functions. J Med Chem. 2006;49(20):5912-5931. https://doi.org/10.1021/jm050362n.
• [30] Wang E, Sun H, Wang J, Wang Z, Liu H, Zhang JZH, Hou T. End-Point Binding Free Energy Calculation with MM/PBSA and MM/GBSA: Strategies and Applications in Drug Design. Chem Rev. 2019;119(16):9478-9508. https://doi.org/10.1021/acs.chemrev.9b00055.
• [31] Hayes J, Archontis G, editors. MM-GB(PB)SA Calculations of Protein-Ligand Binding Free Energies. 2012. https://doi.org/10.5772/37107.
• [32] Wichapong K, Lawson M, Pianwanit S, Kokpol S, Sippl W. Postprocessing of protein-ligand docking poses using linear response MM-PB/SA: application to Wee1 kinase inhibitors. J Chem Inf Model. 2010;50(9):1574-1588. https://doi.org/10.1021/ci1002153.
• [33] Wichapong K, Rohe A, Platzer C, Slynko I, Erdmann F, Schmidt M, Sippl W. Application of docking and QM/MM-GBSA rescoring to screen for novel Myt1 kinase inhibitors. J Chem Inf Model. 2014;54(3):881-893. https://doi.org/10.1021/ci4007326.
• [34] Achary PGR. Applications of Quantitative Structure-Activity Relationships (QSAR) based Virtual Screening in Drug Design: A Review. Mini Rev Med Chem. 2020;20(14):1375-1388. https://doi.org/10.2174/1389557520666200429102334.
• [35] Neves BJ, Braga RC, Melo-Filho CC, Moreira-Filho JT, Muratov EN, Andrade CH. QSAR-Based Virtual Screening: Advances and Applications in Drug Discovery. Front Pharmacol. 2018;9:1275. https://doi.org/10.3389/fphar.2018.01275.
• [36] Tropsha A. QSAR in drug discovery. In: Merz JKM, Ringe D, Reynolds CH, editors. Drug Design: Structure- and Ligand-Based Approaches. Cambridge: Cambridge University Press; 2010. p. 151-64.
• [37] Bastikar V, Bastikar A, Gupta P. Quantitative structure–activity relationship-based computational approaches. Computational Approaches for Novel Therapeutic and Diagnostic Designing to Mitigate SARS-CoV-2 Infection. 2022:191-205. https://doi.org/10.1016/B978-0-323-91172-6.00001-7.,
• [38] Wei Y, Li J, Qing J, Huang M, Wu M, Gao F, Li D, Hong Z, Kong L, Huang W, Lin J. Discovery of Novel Hepatitis C Virus NS5B Polymerase Inhibitors by Combining Random Forest, Multiple e-Pharmacophore Modeling and Docking. PLoS One. 2016;11(2):e0148181. https://doi.org/10.1371/journal.pone.0148181.
• [39] Lazerwith SE, Lew W, Zhang J, Morganelli P, Liu Q, Canales E, Clarke MO, Doerffler E, Byun D, Mertzman M, Ye H, Chong L, Xu L, Appleby T, Chen X, Fenaux M, Hashash A, Leavitt SA, Mabery E, Matles M, Mwangi JW, Tian Y, Lee Y-J, Zhang J, Zhu C, Murray BP, Watkins WJ. Discovery of GS-9669, a Thumb Site II Non-Nucleoside Inhibitor of NS5B for the Treatment of Genotype 1 Chronic Hepatitis C Infection. J Med Chem. 2014;57(5):1893-1901. https://doi.org/10.1021/jm401420j.
• [40] Eltahla AA, Luciani F, White PA, Lloyd AR, Bull RA. Inhibitors of the Hepatitis C Virus Polymerase; Mode of Action and Resistance. Viruses. 2015;7(10):5206-5224. https://doi.org/10.3390/v7102868.
• [41] Wang M, Ng KK, Cherney MM, Chan L, Yannopoulos CG, Bedard J, Morin N, Nguyen-Ba N, Alaoui-Ismaili MH, Bethell RC, James MN. Non-nucleoside analogue inhibitors bind to an allosteric site on HCV NS5B polymerase. Crystal structures and mechanism of inhibition. J Biol Chem. 2003;278(11):9489-9495. https://doi.org/10.1074/jbc.m209397200.
• [42] Nasr T, Aboshanab AM, Mpekoulis G, Drakopoulos A, Vassilaki N, Zoidis G, Abouzid KAM, Zaghary W. Novel 6-Aminoquinazolinone Derivatives as Potential Cross GT1-4 HCV NS5B Inhibitors. Viruses. 2022;14(12):2767. https://doi.org/10.3390/v14122767.
• [43] Elhefnawi M, ElGamacy M, Fares M. Multiple virtual screening approaches for finding new hepatitis C virus RNA-dependent RNA polymerase inhibitors: structure-based screens and molecular dynamics for the pursue of new poly pharmacological inhibitors. BMC Bioinformatics. 2012;13 Suppl 17(Suppl 17):S5. https://doi.org/10.1186/1471-2105-13-s17-s5.
• [44] Beaulieu PL, Coulombe R, Duan J, Fazal G, Godbout C, Hucke O, Jakalian A, Joly MA, Lepage O, Llinàs-Brunet M, Naud J, Poirier M, Rioux N, Thavonekham B, Kukolj G, Stammers TA. Structure-based design of novel HCV NS5B thumb pocket 2 allosteric inhibitors with submicromolar gt1 replicon potency: discovery of a quinazolinone chemotype. Bioorg Med Chem Lett. 2013;23(14):4132-4140. https://doi.org/10.1016/j.bmcl.2013.05.037.
• [45] LaPlante SR, Forgione P, Boucher C, Coulombe R, Gillard J, Hucke O, Jakalian A, Joly MA, Kukolj G, Lemke C, McCollum R, Titolo S, Beaulieu PL, Stammers T. Enantiomeric atropisomers inhibit HCV polymerase and/or HIV matrix: characterizing hindered bond rotations and target selectivity. J Med Chem. 2014;57(5):1944-1951. https://doi.org/10.1021/jm401202a.
• [46] Stammers TA, Coulombe R, Duplessis M, Fazal G, Gagnon A, Garneau M, Goulet S, Jakalian A, LaPlante S, Rancourt J, Thavonekham B, Wernic D, Kukolj G, Beaulieu PL. Anthranilic acid-based Thumb Pocket 2 HCV NS5B polymerase inhibitors with sub-micromolar potency in the cell-based replicon assay. Bioorg Med Chem Lett. 2013;23(24):6879-6885. https://doi.org/10.1016/j.bmcl.2013.09.102.
• [47] Chan L, Reddy TJ, Proulx M, Das SK, Pereira O, Wang W, Siddiqui A, Yannopoulos CG, Poisson C, Turcotte N, Drouin A, Alaoui-Ismaili MH, Bethell R, Hamel M, L'Heureux L, Bilimoria D, Nguyen-Ba N. Identification of N,N-disubstituted phenylalanines as a novel class of inhibitors of hepatitis C NS5B polymerase. J Med Chem. 2003;46(8):1283-1285. https://doi.org/10.1021/jm0340400.
• [48] Reddy TJ, Chan L, Turcotte N, Proulx M, Pereira OZ, Das SK, Siddiqui A, Wang W, Poisson C, Yannopoulos CG, Bilimoria D, L'Heureux L, Alaoui HM, Nguyen-Ba N. Further SAR studies on novel small molecule inhibitors of the hepatitis C (HCV) NS5B polymerase. Bioorg Med Chem Lett. 2003;13(19):3341-3344. https://doi.org/10.1016/s0960-894x(03)00670-x.
• [49] Chan L, Pereira O, Reddy TJ, Das SK, Poisson C, Courchesne M, Proulx M, Siddiqui A, Yannopoulos CG, Nguyen-Ba N, Roy C, Nasturica D, Moinet C, Bethell R, Hamel M, L'Heureux L, David M, Nicolas O, Courtemanche-Asselin P, Brunette S, Bilimoria D, Bédard J. Discovery of thiophene-2-carboxylic acids as potent inhibitors of HCV NS5B polymerase and HCV subgenomic RNA replication. Part 2: tertiary amides. Bioorg Med Chem Lett. 2004;14(3):797-800. https://doi.org/10.1016/j.bmcl.2003.10.068.
• [50] Court JJ, Poisson C, Ardzinski A, Bilimoria D, Chan L, Chandupatla K, Chauret N, Collier PN, Das SK, Denis F, Dorsch W, Iyer G, Lauffer D, L'Heureux L, Li P, Luisi BS, Mani N, Nanthakumar S, Nicolas O, Rao BG, Ronkin S, Selliah S, Shawgo RS, Tang Q, Waal ND, Yannopoulos CG, Green J. Discovery of Novel Thiophene-Based, Thumb Pocket 2 Allosteric Inhibitors of the Hepatitis C NS5B Polymerase with Improved Potency and Physicochemical Profiles. J Med Chem. 2016;59(13):6293-6302. https://doi.org/10.1021/acs.jmedchem.6b00541.
• [51] Eltahla AA, Tay E, Douglas MW, White PA. Cross-genotypic examination of hepatitis C virus polymerase inhibitors reveals a novel mechanism of action for thumb binders. Antimicrob Agents Chemother. 2014;58(12):7215-7224. https://doi.org/10.1128/aac.03699-14.
• [52] Gentile I, Buonomo AR, Zappulo E, Coppola N, Borgia G. GS-9669: a novel non-nucleoside inhibitor of viral polymerase for the treatment of hepatitis C virus infection. Expert Rev Anti Infect Ther. 2014;12(10):1179-1186. https://doi.org/10.1586/14787210.2014.945432.
• [53] Jiang M, Zhang EZ, Ardzinski A, Tigges A, Davis A, Sullivan JC, Nelson M, Spanks J, Dorrian J, Nicolas O, Bartels DJ, Rao BG, Rijnbrand R, Kieffer TL. Genotypic and phenotypic analyses of hepatitis C virus variants observed in clinical studies of VX-222, a nonnucleoside NS5B polymerase inhibitor. Antimicrob Agents Chemother. 2014;58(9):5456-5465. https://doi.org/10.1128/aac.03052-14.
• [54] Xue W, Jiao P, Liu H, Yao X. Molecular modeling and residue interaction network studies on the mechanism of binding and resistance of the HCV NS5B polymerase mutants to VX-222 and ANA598. Antiviral Res. 2014;104:40-51. https://doi.org/10.1016/j.antiviral.2014.01.006.
• [55] Yi G, Deval J, Fan B, Cai H, Soulard C, Ranjith-Kumar CT, Smith DB, Blatt L, Beigelman L, Kao CC. Biochemical study of the comparative inhibition of hepatitis C virus RNA polymerase by VX-222 and filibuvir. Antimicrob Agents Chemother. 2012;56(2):830-837. https://doi.org/10.1128/aac.05438-11.
• [56] Antonysamy SS, Aubol B, Blaney J, Browner MF, Giannetti AM, Harris SF, Hébert N, Hendle J, Hopkins S, Jefferson E, Kissinger C, Leveque V, Marciano D, McGee E, Nájera I, Nolan B, Tomimoto M, Torres E, Wright T. Fragment-based discovery of hepatitis C virus NS5b RNA polymerase inhibitors. Bioorg Med Chem Lett. 2008;18(9):2990-2995. https://doi.org/10.1016/j.bmcl.2008.03.056.
• [57] Barnes-Seeman D, Boiselle C, Capacci-Daniel C, Chopra R, Hoffmaster K, Jones CT, Kato M, Lin K, Ma S, Pan G, Shu L, Wang J, Whiteman L, Xu M, Zheng R, Fu J. Design and synthesis of lactam-thiophene carboxylic acids as potent hepatitis C virus polymerase inhibitors. Bioorg Med Chem Lett. 2014;24(16):3979-3985. https://doi.org/10.1016/j.bmcl.2014.06.031.
• [59] Biswal BK, Wang M, Cherney MM, Chan L, Yannopoulos CG, Bilimoria D, Bedard J, James MN. Non-nucleoside inhibitors binding to hepatitis C virus NS5B polymerase reveal a novel mechanism of inhibition. J Mol Biol. 2006;361(1):33-45. https://doi.org/10.1016/j.jmb.2006.05.074.
• [59] Canales E, Carlson JS, Appleby T, Fenaux M, Lee J, Tian Y, Tirunagari N, Wong M, Watkins WJ. Tri-substituted acylhydrazines as tertiary amide bioisosteres: HCV NS5B polymerase inhibitors. Bioorg Med Chem Lett. 2012;22(13):4288-4292. https://doi.org/10.1016/j.bmcl.2012.05.025
• [60] Hucke O, Coulombe R, Bonneau P, Bertrand-Laperle M, Brochu C, Gillard J, Joly MA, Landry S, Lepage O, Llinàs-Brunet M, Pesant M, Poirier M, Poirier M, McKercher G, Marquis M, Kukolj G, Beaulieu PL, Stammers TA. Molecular dynamics simulations and structure-based rational design lead to allosteric HCV NS5B polymerase thumb pocket 2 inhibitor with picomolar cellular replicon potency. J Med Chem. 2014;57(5):1932-1943. https://doi.org/10.1021/jm4004522.
• [61] Kumar DV, Rai R, Brameld KA, Somoza JR, Rajagopalan R, Janc JW, Xia YM, Ton TL, Shaghafi MB, Hu H, Lehoux I, To N, Young WB, Green MJ. Quinolones as HCV NS5B polymerase inhibitors. Bioorg Med Chem Lett. 2011;21(1):82-87. https://doi.org/10.1016/j.bmcl.2010.11.068.
• [62] Love RA, Parge HE, Yu X, Hickey MJ, Diehl W, Gao J, Wriggers H, Ekker A, Wang L, Thomson JA, Dragovich PS, Fuhrman SA. Crystallographic identification of a noncompetitive inhibitor binding site on the hepatitis C virus NS5B RNA polymerase enzyme. J Virol. 2003;77(13):7575-7581. https://doi.org/10.1128/jvi.77.13.7575-7581.2003
• [63] Stammers TA, Coulombe R, Rancourt J, Thavonekham B, Fazal G, Goulet S, Jakalian A, Wernic D, Tsantrizos Y, Poupart MA, Bös M, McKercher G, Thauvette L, Kukolj G, Beaulieu PL. Discovery of a novel series of non-nucleoside thumb pocket 2 HCV NS5B polymerase inhibitors. Bioorg Med Chem Lett. 2013;23(9):2585-2589. https://doi.org/10.1016/j.bmcl.2013.02.110.
• [64] Yan S, Appleby T, Larson G, Wu JZ, Hamatake R, Hong Z, Yao N. Structure-based design of a novel thiazolone scaffold as HCV NS5B polymerase allosteric inhibitors. Bioorg Med Chem Lett. 2006;16(22):5888-5891. https://doi.org/10.1016/j.bmcl.2006.08.056.
• [65] Yan S, Larson G, Wu JZ, Appleby T, Ding Y, Hamatake R, Hong Z, Yao N. Novel thiazolones as HCV NS5B polymerase allosteric inhibitors: Further designs, SAR, and X-ray complex structure. Bioorg Med Chem Lett. 2007;17(1):63-67. https://doi.org/10.1016/j.bmcl.2006.09.095.
• [66] Yan S, Appleby T, Larson G, Wu JZ, Hamatake RK, Hong Z, Yao N. Thiazolone-acylsulfonamides as novel HCV NS5B polymerase allosteric inhibitors: convergence of structure-based drug design and X-ray crystallographic study. Bioorg Med Chem Lett. 2007;17(7):1991-1995. https://doi.org/10.1016/j.bmcl.2007.01.024.
• [67] Yang H, Hendricks RT, Arora N, Nitzan D, Yee C, Lucas MC, Yang Y, Fung A, Rajyaguru S, Harris SF, Leveque VJ, Hang JQ, Pogam SL, Reuter D, Tavares GA. Cyclic amide bioisosterism: strategic application to the design and synthesis of HCV NS5B polymerase inhibitors. Bioorg Med Chem Lett. 2010;20(15):4614-4619. https://doi.org/10.1016/j.bmcl.2010.06.008.
• [68] Roos K, Wu C, Damm W, Reboul M, Stevenson JM, Lu C, Dahlgren MK, Mondal S, Chen W, Wang L, Abel R, Friesner RA, Harder ED. OPLS3e: Extending Force Field Coverage for Drug-Like Small Molecules. J Chem Theory Comput. 2019;15(3):1863-1874. https://doi.org/10.1021/acs.jctc.8b01026.
• [69] Mayack BK, Alayoubi MM, Gezginci MH. Fingerprint-based QSAR Model Generation to Identify Structural Determinants of HCV NS5B Inhibition. J Res Pharm. 2023; 27(4): 1421-1430. http://dx.doi.org/10.29228/jrp.429.
• [70] Berthold MR, Cebron N, Dill F, Gabriel TR, Kötter T, Meinl T, Ohl P, Sieb C, Thiel K, Wiswedel B, editors. KNIME: The Konstanz Information Miner. Data Analysis, Machine Learning and Applications; 2008 2008//; Berlin, Heidelberg: Springer Berlin Heidelberg.
• [71] Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, Repasky MP, Knoll EH, Shelley M, Perry JK, Shaw DE, Francis P, Shenkin PS. Glide:  A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy. J Med Chem. 2004;47(7):1739-1749. https://doi.org/10.1021/jm0306430.
• [72] Halgren TA, Murphy RB, Friesner RA, Beard HS, Frye LL, Pollard WT, Banks JL. Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J Med Chem. 2004;47(7):1750-1759. https://doi.org/10.1021/jm030644s.
• [73] Peter SC, Dhanjal JK, Malik V, Radhakrishnan N, Jayakanthan M, Sundar D. Quantitative Structure-Activity Relationship (QSAR): Modeling Approaches to Biological Applications. In: Ranganathan S, Gribskov M, Nakai K, Schönbach C, editors. Encyclopedia of Bioinformatics and Computational Biology. Oxford: Academic Press; 2019. p. 661-676.
• [74] Chun H, Keleş S. Sparse partial least squares regression for simultaneous dimension reduction and variable selection. J R Stat Soc Series B Stat Methodol. 2010;72(1):3-25. https://doi.org/10.1111/j.1467-9868.2009.00723.x.
• [75] Allegrini F, Olivieri AC. Two sides of the same coin: Kernel partial least-squares (KPLS) for linear and non-linear multivariate calibration. A tutorial. Talanta Open. 2023;7:100235. https://doi.org/10.1016/j.talo.2023.100235.
• [76] Gu F, Cheung MW. A model-based approach to multivariate principal component regression: Selecting principal components and estimating standard errors for unstandardized regression coefficients. Br J Math Stat Psychol. 2023;76(3):605-622. https://doi.org/10.1111/bmsp.12301.
• [77] Mayack BK. Modeling disruption of Apis mellifera (honey bee) odorant-binding protein function with high-affinity binders. J Mol Recognit. 2023;36(5):e3008. https://doi.org/10.1002/jmr.3008.
• [78] Lu C, Wu C, Ghoreishi D, Chen W, Wang L, Damm W, Ross GA, Dahlgren MK, Russell E, Von Bargen CD, Abel R, Friesner RA, Harder ED. OPLS4: Improving Force Field Accuracy on Challenging Regimes of Chemical Space. J Chem Theory Comput. 2021;17(7):4291-4300. https://doi.org/10.1021/acs.jctc.1c00302.
• [79] Biswal BK, Cherney MM, Wang M, Chan L, Yannopoulos CG, Bilimoria D, Nicolas O, Bedard J, James MN. Crystal structures of the RNA-dependent RNA polymerase genotype 2a of hepatitis C virus reveal two conformations and suggest mechanisms of inhibition by non-nucleoside inhibitors. J Biol Chem. 2005;280(18):18202-18210. https://doi.org/10.1074/jbc.m413410200.
• [80] Ontoria JM, Rydberg EH, Di Marco S, Tomei L, Attenni B, Malancona S, Martin Hernando JI, Gennari N, Koch U, Narjes F, Rowley M, Summa V, Carroll SS, Olsen DB, De Francesco R, Altamura S, Migliaccio G, Carfì A. Identification and biological evaluation of a series of 1H-benzo[de]isoquinoline-1,3(2H)-diones as hepatitis C virus NS5B polymerase inhibitors. J Med Chem. 2009;52(16):5217-5227. https://doi.org/10.1021/jm900517t
• [81] Karaman B, Sippl W. Docking and binding free energy calculations of sirtuin inhibitors. Eur J Med Chem. 2015;93:584-598. https://doi.org/10.1016/j.ejmech.2015.02.045.
• [82] Tropsha A. Best Practices for QSAR Model Development, Validation, and Exploitation. Mol Inform. 2010;29(6-7):476-488. https://doi.org/10.1002/minf.201000061.
• [83] Sedykh A, Zhu H, Tang H, Zhang L, Richard A, Rusyn I, Tropsha A. Use of in vitro HTS-derived concentration-response data as biological descriptors improves the accuracy of QSAR models of in vivo toxicity. Environ Health Perspect. 2011;119(3):364-370. https://doi.org/10.1289/ehp.1002476.
• [84] Falchi F, Bertozzi SM, Ottonello G, Ruda GF, Colombano G, Fiorelli C, Martucci C, Bertorelli R, Scarpelli R, Cavalli A, Bandiera T, Armirotti A. Kernel-Based, Partial Least Squares Quantitative Structure-Retention Relationship Model for UPLC Retention Time Prediction: A Useful Tool for Metabolite Identification. Anal Chem. 2016;88(19):9510-9517. https://doi.org/10.1021/acs.analchem.6b02075.
• [85] Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev. 2001;46(1-3):3-26. https://doi.org/10.1016/s0169-409x(00)00129-0.
• [86] Ghose AK, Viswanadhan VN, Wendoloski JJ. A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases. J Comb Chem. 1999;1(1):55-68. https://doi.org/10.1021/cc9800071.
• [87] Veber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple KD. Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem. 2002;45(12):2615-2623. https://doi.org/10.1021/jm020017n.
• [88] Egan WJ, Merz KM, Jr., Baldwin JJ. Prediction of drug absorption using multivariate statistics. J Med Chem. 2000;43(21):3867-3877. https://doi.org/10.1021/jm000292e.
• [89] Muegge I, Heald SL, Brittelli D. Simple Selection Criteria for Drug-like Chemical Matter. J Med Chem. 2001;44(12):1841-1846. https://doi.org/10.1021/jm015507e.
• [90] Stedman C. Sofosbuvir, a NS5B polymerase inhibitor in the treatment of hepatitis C: A review of its clinical potential. Therap Adv Gastroenterol. 2014;7(3):131-140. https://doi.org/10.1177/1756283x13515825.
• [91] Dennis BB, Naji L, Jajarmi Y, Ahmed A, Kim D. New hope for hepatitis C virus: Summary of global epidemiologic changes and novel innovations over 20 years. World J Gastroenterol. 2021;27(29):4818-4830. https://doi.org/10.3748/wjg.v27.i29.4818.
• [92] Bailey JR, Barnes E, Cox AL. Approaches, Progress, and Challenges to Hepatitis C Vaccine Development. Gastroenterology. 2019;156(2):418-430. https://doi.org/10.1053/j.gastro.2018.08.060.
• [93] Dustin LB. Innate and Adaptive Immune Responses in Chronic HCV Infection. Curr Drug Targets. 2017;18(7):826-843. https://doi.org/10.2174/1389450116666150825110532.
• [94] Martinez MA, Franco S. Therapy Implications of Hepatitis C Virus Genetic Diversity. Viruses. 2020;13(1):41. https://doi.org/10.3390/v13010041.
• [95] Camci M, Şenol H, Kose A, Karaman Mayack B, Alayoubi MM, Karali N, Gezginci MH. Bioisosteric replacement of the carboxylic acid group in Hepatitis-C virus NS5B thumb site II inhibitors: phenylalanine derivatives. Eur J Med Chem. 2024;279:116832. https://doi.org/10.1016/j.ejmech.2024.116832.