The index of ideality of correlation: QSAR studies of hepatitis C virus NS3/4A protease inhibitors using SMILES descriptors

References

• M.J. Evans, T. Von Hahn, D.M. Tscherne, A.J. Syder, M. Panis, B. Wölk, T. Hatziioannou, J.A. McKeating, P.D. Bieniasz, and C.M. Rice, Claudin-1 is a hepatitis C virus co-receptor required for a late step in entry, Nature 446 (2007), pp. 801–805. doi:10.1038/nature05654.
   PubMed Web of Science ®Google Scholar
• D. Moradpour, F. Penin, and C.M. Rice, Replication of hepatitis C virus, Nat. Rev. Microbiol. 5 (2007), pp. 453–463.
   PubMed Web of Science ®Google Scholar
• S. Ahmadi, M.R. Khazaei, and A. Abdolmaleki, Quantitative structure–property relationship study on the intercalation of anticancer drugs with ct-DNA, Med. Chem. Res. 23 (2014), pp. 1148–1161. doi:10.1007/s00044-013-0716-z.
   Web of Science ®Google Scholar
• J.B. Ghasemi, S. Ahmadi, and S. Brown, A quantitative structure–retention relationship study for prediction of chromatographic relative retention time of chlorinated monoterpenes, Environ. Chem. Lett. 9 (2011), pp. 87–96. doi:10.1007/s10311-009-0251-9.
   Web of Science ®Google Scholar
• J.B. Ghasemi, S. Ahmadi, and M. Ayati, QSPR modeling of stability constants of the Li-hemispherands complexes using MLR: A theoretical host-guest study, Macroheterocycles 3 (2010), pp. 234–242. doi:10.6060/mhc2010.4.234.
   Google Scholar
• A.Z. Dudek, T. Arodz, and J. Gálvez, Computational methods in developing quantitative structure-activity relationships (QSAR): A review, Comb. Chem. High T. Scr. 9 (2006), pp. 213–228.
   PubMed Web of Science ®Google Scholar
• K. Roy and R.N. Das, A review on principles, theory and practices of 2D-QSAR, Curr. Drug Metab. 15 (2014), pp. 346–379. doi:10.2174/1389200215666140908102230.
   PubMed Web of Science ®Google Scholar
• P. Gramatica and A. Sangion, A historical excursus on the statistical validation parameters for QSAR models: A clarification concerning metrics and terminology, J. Chem. Inf. Model. 56 (2016), pp. 1127–1131.
   PubMed Web of Science ®Google Scholar
• A. Kumar, J. Sindhu, and P. Kumar, In-silico identification of fingerprint of pyrazolyl sulfonamide responsible for inhibition of N-myristoyltransferase using Monte Carlo method with index of ideality of correlation, J. Biomol. Struct. Dyn. (2020). doi: 10.1080/07391102.2020.1784286.
   Web of Science ®Google Scholar
• P. Kumar and A. Kumar, Nucleobase sequence based building up of reliable QSAR models with the index of ideality correlation using Monte Carlo method, J. Biomol. Struct. Dyn. 38 (2019), pp. 3296–3306. doi:10.1080/07391102.2019.1656109.
   PubMed Web of Science ®Google Scholar
• M. Duhan, R. Singh, M. Devi, J. Sindhu, R. Bhatia, A. Kumar, and P. Kumar, Synthesis, molecular docking and QSAR study of thiazole clubbed pyrazole hybrid as α-amylase inhibitor, J. Biomol. Struct. Dyn. 39 (2021), pp. 91–107. doi:10.1080/07391102.2019.1704885.
   PubMed Web of Science ®Google Scholar
• V.P. Ničković, Z.N. Vujnović-Živković, R. Trajković, D. Krtinić, L. Ristić, M. Radović, Z. Ćirić, D. Sokolović, and A.M. Veselinović, In silico studies and the design of novel agents for the treatment of systemic tuberculosis, J. Biomol. Struct. Dyn. 37 (2019), pp. 3198–3205. doi:10.1080/07391102.2018.1511476.
   PubMed Web of Science ®Google Scholar
• J. Zhu, Y. Li, H. Yu, L. Zhang, X. Mao, and T. Hou, Insight into the structural requirements of narlaprevir-type inhibitors of NS3/NS4A protease based on HQSAR and molecular field analyses, Comb. Chem. High T. Scr. 15 (2012), pp. 439–450.
   PubMed Web of Science ®Google Scholar
• E.F.F. Da Cunha, K.S. Matos, and T.C. Ramalho, QSAR and docking studies of HCV NS3 serine protease inhibitors, Med. Chem. 9 (2013), pp. 774–805. doi:10.2174/1573406411309060003.
   PubMed Web of Science ®Google Scholar
• P. Kumar, A. Kumar, and J. Sindhu, In silico design of diacylglycerol acyltransferase-1 (DGAT1) inhibitors based on SMILES descriptors using Monte-Carlo method, SAR QSAR Environ. Res. 30 (2019), pp. 525–541. doi:10.1080/1062936X.2019.1629998.
   PubMed Web of Science ®Google Scholar
• P. Kumar and A. Kumar, CORAL: QSAR models of CB1 cannabinoid receptor inhibitors based on local and global SMILES attributes with the index of ideality of correlation and the correlation contradiction index, Chemom. Intell. Lab. 200 (2020), pp. 103982. doi:10.1016/j.chemolab.2020.103982.
   Web of Science ®Google Scholar
• S. Ahmadi, A.P. Toropova, and A.A. Toropov, Correlation intensity index: Mathematical modeling of cytotoxicity of metal oxide nanoparticles, Nanotoxicology 14 (2020), pp. 1118–1126. doi:10.1080/17435390.2020.1808252.
   PubMed Web of Science ®Google Scholar
• S. Ahmadi and A. Akbari, Prediction of the adsorption coefficients of some aromatic compounds on multi-wall carbon nanotubes by the Monte Carlo method, SAR QSAR Environ. Res. 29 (2018), pp. 895–909. doi:10.1080/1062936X.2018.1526821.
   PubMed Web of Science ®Google Scholar
• S. Lotfi, S. Ahmadi, and P. Zohrabi, QSAR modeling of toxicities of ionic liquids toward Staphylococcus aureus using SMILES and graph invariants, Struct. Chem. 31 (2020), pp. 2257–2270. doi:10.1007/s11224-020-01568-y.
   Web of Science ®Google Scholar
• S. Ahmadi, S. Lotfi, and P. Kumar, A Monte Carlo method based QSPR model for prediction of reaction rate constants of hydrated electrons with organic contaminants, SAR QSAR Environ. Res. 31 (2020), pp. 935–950. doi:10.1080/1062936X.2020.1842495.
   PubMed Web of Science ®Google Scholar
• F.-R. Alexandre, G. Brandt, C. Caillet, D. Chaves, T. Convard, M. Derock, D. Gloux, Y. Griffon, L. Lallos, and F. Leroy, Synthesis and antiviral evaluation of a novel series of homoserine-based inhibitors of the hepatitis C virus NS3/4A serine protease, Bioorg. Med. Chem. Lett. 25 (2015), pp. 3984–3991. doi:10.1016/j.bmcl.2015.07.020.
   PubMedGoogle Scholar
• R. Beevers, M.G. Carr, P.S. Jones, S. Jordan, P.B. Kay, R.C. Lazell, and T.M. Raynham, Solution and solid-Phase synthesis of potent inhibitors of hepatitis C Virus NS3 proteinase, Bioorg. Med. Chem. Lett. 12 (2002), pp. 641–643. doi:10.1016/S0960-894X(01)00816-2.
   PubMed Web of Science ®Google Scholar
• J. Bennett, A. Campbell, A. Campbell, M. Carr, R. Dunsdon, J. Greening, D. Hurst, N. Jennings, P. Jones, and S. Jordan, The identification of α-ketoamides as potent inhibitors of hepatitis c virus nS3-4a proteinase, Bioorg. Med. Chem. Lett. 11 (2001), pp. 355–357. doi:10.1016/S0960-894X(00)00654-5.
   PubMed Web of Science ®Google Scholar
• F. Bilodeau, M.D. Bailey, P.K. Bhardwaj, J. Bordeleau, P. Forgione, M. Garneau, E. Ghiro, V. Gorys, T. Halmos, and E.S. Jolicoeur, Synthesis and optimization of a novel series of HCV NS3 protease inhibitors: 4-Arylproline analogs, Bioorg. Med. Chem. Lett. 23 (2013), pp. 4267–4271. doi:10.1016/j.bmcl.2013.03.043.
   PubMedGoogle Scholar
• X.C. Sheng, T. Appleby, T. Butler, R. Cai, X. Chen, A. Cho, M.O. Clarke, J. Cottell, W.E. Delaney IV, and E. Doerffler, Discovery of GS-9451: An acid inhibitor of the hepatitis C virus NS3/4A protease, Bioorg. Med. Chem. Lett. 22 (2012), pp. 2629–2634. doi:10.1016/j.bmcl.2012.01.017.
   PubMed Web of Science ®Google Scholar
• M.O. Clarke, X. Chen, A. Cho, W.E. Delaney IV, E. Doerffler, M. Fardis, M. Ji, M. Mertzman, R. Pakdaman, and H.-J. Pyun, Novel, potent, and orally bioavailable phosphinic acid inhibitors of the hepatitis C virus NS3 protease, Bioorg. Med. Chem. Lett. 21 (2011), pp. 3568–3572. doi:10.1016/j.bmcl.2011.04.125.
   PubMed Web of Science ®Google Scholar
• M.O. Clarke, D. Byun, X. Chen, E. Doerffler, S.A. Leavitt, X.C. Sheng, C.Y. Yang, and C.U. Kim, Novel, potent and orally bioavailable indolizidinone-derived inhibitors of the hepatitis C virus NS3 protease, Bioorg. Med. Chem. Lett. 22 (2012), pp. 1095–1098. doi:10.1016/j.bmcl.2011.11.107.
   PubMed Web of Science ®Google Scholar
• C.Z. Ding, Y.-K. Zhang, X. Li, Y. Liu, S. Zhang, Y. Zhou, J.J. Plattner, S.J. Baker, L. Liu, and M. Duan, Synthesis and biological evaluations of P4-benzoxaborole-substituted macrocyclic inhibitors of HCV NS3 protease, Bioorg. Med. Chem. Lett. 20 (2010), pp. 7317–7322. doi:10.1016/j.bmcl.2010.10.071.
   PubMed Web of Science ®Google Scholar
• P.W. Glunz, B.D. Douty, and C.P. Decicco, Design and synthesis of bicyclic pyrimidinone-based HCV NS3 protease inhibitors, Bioorg. Med. Chem. Lett. 13 (2003), pp. 785–788. doi:10.1016/S0960-894X(03)00022-2.
   PubMedGoogle Scholar
• N. Goudreau, D.R. Cameron, P. Bonneau, V. Gorys, C. Plouffe, M. Poirier, D. Lamarre, and M. Llinas-Brunet, NMR structural characterization of peptide inhibitors bound to the Hepatitis C virus NS3 protease: Design of a new P2 substituent, J. Med. Chem. 47 (2004), pp. 123–132. doi:10.1021/jm0303002.
   PubMed Web of Science ®Google Scholar
• W. Han, Z. Hu, X. Jiang, and C.P. Decicco, α-Ketoamides, α-ketoesters and α-diketones as HCV NS3 protease inhibitors, Bioorg. Med. Chem. Lett. 10 (2000), pp. 711–713. doi:10.1016/S0960-894X(00)00074-3.
   PubMed Web of Science ®Google Scholar
• W. Han, Z. Hu, X. Jiang, Z.R. Wasserman, and C.P. Decicco, Glycine α-ketoamides as HCV NS3 protease inhibitors, Bioorg. Med. Chem. Lett. 13 (2003), pp. 1111–1114. doi:10.1016/S0960-894X(03)00031-3.
   PubMed Web of Science ®Google Scholar
• W. Han, X. Jiang, Z. Hu, Z.R. Wasserman, and C.P. Decicco, Investigation of glycine α-ketoamide HCV NS3 protease inhibitors: Effect of carboxylic acid isosteres, Bioorg. Med. Chem. Lett. 15 (2005), pp. 3487–3490.
   PubMed Web of Science ®Google Scholar
• W.M. Kazmierski, R. Hamatake, M. Duan, L.L. Wright, G.K. Smith, R.L. Jarvest, -J.-J. Ji, J.P. Cooper, M.D. Tallant, and R.M. Crosby, Discovery of novel urea-based hepatitis C protease inhibitors with high potency against protease-inhibitor-resistant mutants, J. Med. Chem. 55 (2012), pp. 3021–3026. doi:10.1021/jm201278q.
   PubMedGoogle Scholar
• X. Li, Y.-K. Zhang, Y. Liu, C.Z. Ding, Q. Li, Y. Zhou, J.J. Plattner, S.J. Baker, X. Qian, and D. Fan, Synthesis and evaluation of novel α-amino cyclic boronates as inhibitors of HCV NS3 protease, Bioorg. Med. Chem. Lett. 20 (2010), pp. 3550–3556. doi:10.1016/j.bmcl.2010.04.129.
   PubMedGoogle Scholar
• X. Li, Y.-K. Zhang, Y. Liu, C.Z. Ding, Y. Zhou, Q. Li, J.J. Plattner, S.J. Baker, S. Zhang, and W.M. Kazmierski, Novel macrocyclic HCV NS3 protease inhibitors derived from α-amino cyclic boronates, Bioorg. Med. Chem. Lett. 20 (2010), pp. 5695–5700. doi:10.1016/j.bmcl.2010.08.022.
   PubMed Web of Science ®Google Scholar
• X. Li, Y.-K. Zhang, Y. Liu, S. Zhang, C.Z. Ding, Y. Zhou, J.J. Plattner, S.J. Baker, L. Liu, and W. Bu, Synthesis of new acylsulfamoyl benzoxaboroles as potent inhibitors of HCV NS3 protease, Bioorg. Med. Chem. Lett. 20 (2010), pp. 7493–7497. doi:10.1016/j.bmcl.2010.10.007.
   PubMedGoogle Scholar
• X. Li, S. Zhang, Y.-K. Zhang, Y. Liu, C.Z. Ding, Y. Zhou, J.J. Plattner, S.J. Baker, W. Bu, and L. Liu, Synthesis and SAR of acyclic HCV NS3 protease inhibitors with novel P4-benzoxaborole moieties, Bioorg. Med. Chem. Lett. 21 (2011), pp. 2048–2054. doi:10.1016/j.bmcl.2011.02.006.
   PubMed Web of Science ®Google Scholar
• X. Li, Y. Liu, Y.-K. Zhang, J.J. Plattner, S.J. Baker, W. Bu, L. Liu, Y. Zhou, C.Z. Ding, and S. Zhang, Synthesis and antiviral activity of novel HCV NS3 protease inhibitors with P4 capping groups, Bioorg. Med. Chem. Lett. 22 (2012), pp. 7351–7356. doi:10.1016/j.bmcl.2012.10.075.
   PubMedGoogle Scholar
• M. Llinas-Brunet, M. Bailey, G. Fazal, S. Goulet, T. Halmos, S. Laplante, R. Maurice, M. Poirier, M.-A. Poupart, and D. Thibeault, Peptide-based inhibitors of the hepatitis C virus serine protease, Bioorg. Med. Chem. Lett. 8 (1998), pp. 1713–1718. doi:10.1016/S0960-894X(98)00299-6.
   PubMed Web of Science ®Google Scholar
• M. Llinàs-Brunet, M. Bailey, R. Déziel, G. Fazal, V. Gorys, S. Goulet, T. Halmos, R. Maurice, M. Poirier, and M.-A. Poupart, Studies on the C-terminal of hexapeptide inhibitors of the hepatitis C virus serine protease, Bioorg. Med. Chem. Lett. 8 (1998), pp. 2719–2724. doi:10.1016/S0960-894X(98)00480-6.
   PubMed Web of Science ®Google Scholar
• M. Llinàs-Brunet, M. Bailey, G. Fazal, E. Ghiro, V. Gorys, S. Goulet, T. Halmos, R. Maurice, M. Poirier, and M.-A. Poupart, Highly potent and selective peptide-based inhibitors of the hepatitis C virus serine protease: Towards smaller inhibitors, Bioorg. Med. Chem. Lett. 10 (2000), pp. 2267–2270. doi:10.1016/S0960-894X(00)00465-0.
   PubMed Web of Science ®Google Scholar
• M. Llinas-Brunet, M.D. Bailey, G. Bolger, C. Brochu, A.-M. Faucher, J.M. Ferland, M. Garneau, E. Ghiro, V. Gorys, and C. Grand-Maître, Structure− activity study on a novel series of macrocyclic inhibitors of the hepatitis C virus NS3 protease leading to the discovery of BILN 2061, J. Med. Chem. 47 (2004), pp. 1605–1608. doi:10.1021/jm0342414.
   PubMed Web of Science ®Google Scholar
• M. Llinas-Brunet, M.D. Bailey, N. Goudreau, P.K. Bhardwaj, J. Bordeleau, M. Bös, Y. Bousquet, M.G. Cordingley, J. Duan, and P. Forgione, Discovery of a potent and selective noncovalent linear inhibitor of the hepatitis C virus NS3 protease (BI 201335), J. Med. Chem. 53 (2010), pp. 6466–6476. doi:10.1021/jm100690x.
   PubMed Web of Science ®Google Scholar
• C.C. Parsy, F.-R. Alexandre, V. Bidau, F. Bonnaterre, G. Brandt, C. Caillet, S. Cappelle, D. Chaves, T. Convard, and M. Derock, Discovery and structural diversity of the hepatitis C virus NS3/4A serine protease inhibitor series leading to clinical candidate IDX320, Bioorg. Med. Chem. Lett. 25 (2015), pp. 5427–5436. doi:10.1016/j.bmcl.2015.09.009.
   PubMed Web of Science ®Google Scholar
• Z. Qin, M. Wang, and A. Yan, QSAR studies of the bioactivity of hepatitis C virus (HCV) NS3/4A protease inhibitors by multiple linear regression (MLR) and support vector machine (SVM), Bioorg. Med. Chem. Lett. 27 (2017), pp. 2931–2938. doi:10.1016/j.bmcl.2017.05.001.
   PubMed Web of Science ®Google Scholar
• J.T. Randolph, X. Zhang, P.P. Huang, L.L. Klein, K.A. Kurtz, A.K. Konstantinidis, W. He, W.M. Kati, and D.J. Kempf, Synthesis, antiviral activity, and conformational studies of a P3 aza-peptide analog of a potent macrocyclic tripeptide HCV protease inhibitor, Bioorg. Med. Chem. Lett. 18 (2008), pp. 2745–2750. doi:10.1016/j.bmcl.2008.02.053.
   PubMed Web of Science ®Google Scholar
• P.M. Scola, A.X. Wang, A.C. Good, L.-Q. Sun, K.D. Combrink, J.A. Campbell, J. Chen, Y. Tu, N. Sin, and B.L. Venables, Discovery and early clinical evaluation of BMS-605339, a potent and orally efficacious tripeptidic acylsulfonamide NS3 protease inhibitor for the treatment of hepatitis C virus infection, J. Med. Chem. 57 (2014), pp. 1708–1729. doi:10.1021/jm401840s.
   PubMed Web of Science ®Google Scholar
• X.C. Sheng, H.-J. Pyun, K. Chaudhary, J. Wang, E. Doerffler, M. Fleury, D. McMurtrie, X. Chen, W.E. Delaney IV, and C.U. Kim, Discovery of novel phosphonate derivatives as hepatitis C virus NS3 protease inhibitors, Bioorg. Med. Chem. Lett. 19 (2009), pp. 3453–3457. doi:10.1016/j.bmcl.2009.05.023.
   PubMed Web of Science ®Google Scholar
• F. Shi, Y. Zhang, and W. Xu, Discovery of a series of novel compounds with moderate anti-hepatitis C virus NS3 protease activity in vitro, Bioorg. Med. Chem. 23 (2015), pp. 5539–5545. doi:10.1016/j.bmc.2015.07.032.
   PubMed Web of Science ®Google Scholar
• X. Zhang, A.C. Schmitt, W. Jiang, Z. Wasserman, and C.P. Decicco, Design and synthesis of potent, non-peptide inhibitors of HCV NS3 protease, Bioorg. Med. Chem. Lett. 13 (2003), pp. 1157–1160. doi:10.1016/S0960-894X(03)00032-5.
   PubMedGoogle Scholar
• A.M. Veselinović, A. Toropov, A. Toropova, D. Stanković-Đorđević, and J.B. Veselinović, Design and development of novel antibiotics based on FtsZ inhibition–in silico studies, New J. Chem. 42 (2018), pp. 10976–10982. doi:10.1039/C8NJ01034J.
   Web of Science ®Google Scholar
• A.A. Toropov and A.P. Toropova, Predicting cytotoxicity of 2-phenylindole derivatives against breast cancer cells using index of ideality of correlation, Anticancer Res. 38 (2018), pp. 6189–6194. doi:10.21873/anticanres.12972.
   PubMed Web of Science ®Google Scholar
• S. Ahmadi, Mathematical modeling of cytotoxicity of metal oxide nanoparticles using the index of ideality correlation criteria, Chemosphere 242 (2020), pp. 125192. doi:10.1016/j.chemosphere.2019.125192.
   PubMed Web of Science ®Google Scholar
• A.P. Toropova, A.A. Toropov, D. Leszczynska, and J. Leszczynski, How the CORAL software can be used to select compounds for efficient treatment of neurodegenerative diseases? Toxicol. Appl. Pharm. 408 (2020), pp. 115276. doi:10.1016/j.taap.2020.115276.
   PubMed Web of Science ®Google Scholar
• S. Ahmadi, E. Babaee, and M.R. Khazaei, Application of self organizing maps and GA-MLR for the estimation of stability constant of 18-crown-6 ether derivatives with sodium cation, J. Incl. Phenom. Macro. 79 (2014), pp. 141–149. doi:10.1007/s10847-013-0337-7.
   Web of Science ®Google Scholar
• S. Ahmadi and E. Habibpour, Application of GA-MLR for QSAR modeling of the arylthioindole class of tubulin polymerization inhibitors as anticancer agents, Anti-Cancer Agent Me. 17 (2017), pp. 552–565. doi:10.2174/1871520616666160811162105.
   PubMed Web of Science ®Google Scholar
• S. Ahmadi, Application of GA-MLR method in QSPR modeling of stability constants of diverse 15-crown-5 complexes with sodium cation, J. Incl. Phenom. Macro. 74 (2012), pp. 57–66. doi:10.1007/s10847-010-9881-6.
   Web of Science ®Google Scholar
• R.B. Aher and K. Roy, Exploring the structural requirements in multiple chemical scaffolds for the selective inhibition of Plasmodium falciparum calcium-dependent protein kinase-1 (Pf CDPK-1) by 3D-pharmacophore modelling, and docking studies, SAR and QSAR Environ. Res. 28 (2017), pp. 390–414. doi:10.1080/1062936X.2017.1326401.
   PubMed Web of Science ®Google Scholar
• S.S. Bhayye, K. Roy, and A. Saha, Pharmacophore generation, atom-based 3D-QSAR, HQSAR and activity cliff analyses of benzothiazine and deazaxanthine derivatives as dual A 2A antagonists/MAO‑B inhibitors, SAR QSAR Environ. Res. 27 (2016), pp. 183–202. doi:10.1080/1062936X.2015.1136840.
   PubMed Web of Science ®Google Scholar
• A. Golbraikh and A. Tropsha, Beware of q2!, J. Mol. Graph. Model. 20 (2002), pp. 269–276. doi:10.1016/S1093-3263(01)00123-1.
   PubMed Web of Science ®Google Scholar
• L.M. Shi, H. Fang, W. Tong, J. Wu, R. Perkins, R.M. Blair, W.S. Branham, S.L. Dial, C.L. Moland, and D.M. Sheehan, QSAR models using a large diverse set of estrogens, J. Chem. Inf. Comput. Sci. 41 (2001), pp. 186–195. doi:10.1021/ci000066d.
   PubMedGoogle Scholar
• G. Schuurmann, R.U. Ebert, J. Chen, B. Wang, and R. Kuhne, External validation and prediction employing the predictive squared correlation coefficient test set activity mean vs training set activity mean, J. Chem. Inf. Model. 48 (2008), pp. 2140–2145. doi:10.1021/ci800253u.
   PubMed Web of Science ®Google Scholar
• P.P. Roy, S. Paul, I. Mitra, and K. Roy, On two novel parameters for validation of predictive QSAR models, Molecules 14 (2009), pp. 1660–1701.
   PubMed Web of Science ®Google Scholar
• K. Roy, P. Chakraborty, I. Mitra, P.K. Ojha, S. Kar, and R.N. Das, Some case studies on application of “r(m)2” metrics for judging quality of quantitative structure-activity relationship predictions: Emphasis on scaling of response data, J. Comput. Chem. 34 (2013), pp. 1071–1082. doi:10.1002/jcc.23231.
   PubMed Web of Science ®Google Scholar
• N. Chirico and P. Gramatica, Real external predictivity of QSAR models: How to evaluate it? Comparison of different validation criteria and proposal of using the concordance correlation coefficient, J. Chem. Inf. Model. 51 (2011), pp. 2320–2335. doi:10.1021/ci200211n.
   PubMed Web of Science ®Google Scholar
• K. Roy, S. Kar, and R.N. Das, A Primer on QSAR/QSPR Modeling: Fundamental Concepts, Berlin/Heidelberg (Germany): Springer, 2015.
   Google Scholar
• A.A. Toropov and A.P. Toropova, Correlation intensity index: Building up models for mutagenicity of silver nanoparticles, Sci. Total Environ. 737 (2020), pp. 139720. doi:10.1016/j.scitotenv.2020.139720.
   PubMed Web of Science ®Google Scholar
• K. Jafari, M.H. Fatemi, A.P. Toropova, and A.A. Toropov, Correlation Intensity Index (CII) as a criterion of predictive potential: Applying to model thermal conductivity of metal oxide-based ethylene glycol nanofluids, Chem. Phys. Lett. 754 (2020), pp. 137614. doi:10.1016/j.cplett.2020.137614.
   Web of Science ®Google Scholar
• A.P. Toropova, A.A. Toropov, D. Leszczynska, and J. Leszczynski, The index of ideality of correlation: Models of the flash points of ternary mixtures, New J. Chem. 44 (2020), pp. 4858–4868. doi:10.1039/D0NJ00121J.
   Web of Science ®Google Scholar
• A. Kumar and P. Kumar, Construction of pioneering quantitative structure activity relationship screening models for abuse potential of designer drugs using index of ideality of correlation in monte carlo optimization, Arch. Toxicol. 94 (2020), pp. 3069–3086. doi:10.1007/s00204-020-02828-w.
   PubMed Web of Science ®Google Scholar
• A.P. Toropova, A.A. Toropov, A.M. Veselinović, J.B. Veselinović, E. Benfenati, D. Leszczynska, and J. Leszczynski, Nano-QSAR: Model of mutagenicity of fullerene as a mathematical function of different conditions, Ecotox. Environ. Safe. 124 (2016), pp. 32–36. doi:10.1016/j.ecoenv.2015.09.038.
   PubMed Web of Science ®Google Scholar
• M. Javidfar and S. Ahmadi, QSAR modelling of larvicidal phytocompounds against Aedes aegypti using index of ideality of correlation, SAR QSAR Environ. Res. 31 (2020), pp. 717–739. doi:10.1080/1062936X.2020.1806922.
   PubMed Web of Science ®Google Scholar
• S. Ahmadi, F. Mardinia, N. Azimi, M. Qomi, and E. Balali, Prediction of chalcone derivative cytotoxicity activity against MCF-7 human breast cancer cell by Monte Carlo method, J. Mol. Struct. 1181 (2019), pp. 305–311. doi:10.1016/j.molstruc.2018.12.089.
   Web of Science ®Google Scholar
• A.A. Toropov, N. Sizochenko, A.P. Toropova, D. Leszczynska, and J. Leszczynski, Advancement of predictive modeling of zeta potentials (ζ) in metal oxide nanoparticles with correlation intensity index (CII), J. Mol. Liq. 317 (2020), pp. 113929. doi:10.1016/j.molliq.2020.113929.
   Web of Science ®Google Scholar
• S. Harper, J.A. McCauley, M.T. Rudd, M. Ferrara, M. DiFilippo, B. Crescenzi, U. Koch, A. Petrocchi, M.K. Holloway, and J.W. Butcher, Discovery of MK-5172, a macrocyclic hepatitis C virus NS3/4a protease inhibitor, ACS Med. Chem. Lett. 3 (2012), pp. 332–336. doi:10.1021/ml300017p.
   PubMed Web of Science ®Google Scholar
• J. Lalezari, J.G. Sullivan, P. Varunok, E. Galen, K.V. Kowdley, V. Rustgi, H. Aguilar, F. Felizarta, B. McGovern, and M. King, Ombitasvir/paritaprevir/r and dasabuvir plus ribavirin in HCV genotype 1-infected patients on methadone or buprenorphine, J. Hepatol. 63 (2015), pp. 364–369. doi:10.1016/j.jhep.2015.03.029.
   PubMed Web of Science ®Google Scholar
• Å. Rosenquist, B. Samuelsson, P.-O. Johansson, M.D. Cummings, O. Lenz, P. Raboisson, K. Simmen, S. Vendeville, H. De Kock, and M. Nilsson, Discovery and development of simeprevir (TMC435), a HCV NS3/4A protease inhibitor, J. Med. Chem. 57 (2014), pp. 1673–1693. doi:10.1021/jm401507s.
   PubMed Web of Science ®Google Scholar
• D.I. Soumana, A. Ali, and C.A. Schiffer, Structural analysis of asunaprevir resistance in HCV NS3/4A protease, ACS Chem. Biol. 9 (2014), pp. 2485–2490. doi:10.1021/cb5006118.
   PubMed Web of Science ®Google Scholar
• C. Lin, A. Kwong, and R. Perni, Discovery and development of VX-950, a novel, covalent, and reversible inhibitor of hepatitis C virus NS3. 4A serine protease, Infect. Disord. Drug Targets 6 (2006), pp. 3–16. doi:10.2174/187152606776056706.
   PubMedGoogle Scholar
• N.C. Comelli, E.V. Ortiz, M. Kolacz, A.P. Toropova, A.A. Toropov, P.R. Duchowicz, and E.A. Castro, Conformation-independent QSAR on c-Src tyrosine kinase inhibitors, Chemom. Intell. Lab. 134 (2014), pp. 47–52. doi:10.1016/j.chemolab.2014.03.003.
   Web of Science ®Google Scholar