基于机器学习方法的丙型肝炎病毒聚合酶NS5B非核苷抑制剂的定量构效关系研究

doi:10.3866/PKU.WHXB201305171

Abstract

Abstract:

The quantitative structure-activity relationship (QSAR) approach was used to predict the activity of two different scaffolds (benzoisothiazole and benzothiazine) of 89 non-nucleoside inhibitors of hepatitis c virus (HCV) NS5B polymerase. Two selection methods, linear stepwise regression analysis (LSRA) and genetic algorithm-partial least squares (GA-PLS), were used to select appropriate descriptor subsets for QSAR modeling with linear models. The genetic algorithm-support vector machine (GA-SVM) approach was first used to build nonlinear models with six LSRA- and seven GA-PLS-selected descriptors. Three QSAR models built with the six LSRA-selected descriptors gave correlation coefficients of 0.958-0.962 for the training set. GA-SVM provided the highest prediction accuracy of the models of 0.962. Three QSAR models built with the seven GA-PLS-selected descriptors gave correlation coefficients of 0.918-0.960 for the training set, of which the partial least squares (PLS) model was the best (0.960). The investigated models gave satisfactory prediction results and can be extended to other QSAR studies.

Key words: HCV NS5B polymerase, Non-nucleoside Inhibitor, Linear stepwise regression analysis, Partial least square, Genetic algorithm, Support vector machine

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CONG Yong, XUE Ying. Quantitative Structure-Activity Relationship Study of the Non-Nucleoside Inhibitors of HCV NS5B Polymerase by Machine Learning Methods[J]. Acta Phys. -Chim. Sin. 2013, 29(08), 1639-1647. doi: 10.3866/PKU.WHXB201305171

References

(1) Choo, Q. L.;Weiner, A. J.; Overby, L. R.; Bradley, D.W.;Houghton, M. Science 1989, 244, 359. doi: 10.1126/science.2523562
(2) (a) Lauer, G. M.;Walker, B. D. N. Engl. J. Med. 2001, 345, 41.doi: 10.1056/NEJM200107053450107
(b) Di Bisceglie, A. M. Lancet 1998, 351, 351.
(c) Alter, M. J.; Kruszon-Moran, D.; Nainan, O. V.; McQuillan,G. M.; Gao, F.; Moyer, L. A.; Kaslow, R. A.; Margolis, H. S.N. Engl. J. Med. 1999, 341, 556.
(3) Manns, M. P.; McHutchison, J. G.; Gordon, S. C.; Rustgi, V. K.;Shiffman, M.; Reindollar, R.; Goodman, Z. D.; Koury, K.; Ling,M. H.; Albrecht, J. K. Lancet 2002, 347, 975.
(4) (a) Koch, U.; Narjes, F. Curr. Top. Med. Chem. 2007, 7, 1302.doi: 10.2174/156802607781212211
(b) Rönn, R.; Sandström, A. Curr. Top. Med. Chem. 2008, 8, 533.
(c) Zapf, C.W.; Bloom, J. D.; Levin, J. I. Ann. Rep. Med. Chem.2007, 42, 281.
(5) Appel, N.; Schaller, T.; Penin, F.; Bartenschlager, R. J. Biol. Chem. 2006, 281, 9833. doi: 10.1074/jbc.R500026200
(6) Ni, Z. J.;Wagman, A. S. Curr. Opin. Drug Discov. Dev. 2004, 7,446.
(7) Beaulieu, P. L.; Bos, M.; Bousquet, Y.; Fazal, G.; Gauthier, J.;Gillard, J.; Goulet, S.; LaPlante, S.; Poupart, M. A.; Lefebvre,S.; McKercher, G.; Pellerin, C.; Austel, V.; Kukolj, G. Bioorg. Med. Chem. Lett. 2004, 14, 119. doi: 10.1016/j.bmcl.2003.10.023
(8) Stansfield, I.; Ercolani, C.; Mackay, A.; Conte, I.; Pompei, M.;Koch, U.; Gennari, N.; Giuliano, C.; Rowley, M.; Narjes, F.Bioorg. Med. Chem. Lett. 2009, 19, 627. doi: 10.1016/j.bmcl.2008.12.068
(9) Louise-May, S.; Yang,W.; Nie, X.; Liu, D.; Deshpande, M. S.;Phadke, A. S.; Huang, M.; Agarwal, A. Bioorg. Med. Chem. Lett. 2007, 17, 3905. doi: 10.1016/j.bmcl.2007.04.103
(10) Stankiewicz-Drogon, A.; Palchykovska, L. G.; Kostina, V. G.;Alexeeva, I. V.; Shved, A. D.; Boguszewska-Chachulska, A. M.Bioorg. Med. Chem. 2008, 16, 8846. doi: 10.1016/j.bmc.2008.08.074
(11) Bosse, T. D.; Larson, D. P.;Wagner, R.; Hutchinson, D. K.;Rockway, T.W.; Kati,W. M.; Liu, Y.; Masse, S.; Middleton, T.;Mo, H.; Montgomery, D.; Jiang,W.; Koev, G.; Kempf, D. J.;Molla, A. Bioorg. Med. Chem. Lett. 2008, 18, 568. doi: 10.1016/j.bmcl.2007.11.088
(12) Lü,W. J.; Chen, Y. L.; Ma,W. P.; Zhang, X. Y.; Luan, F.; Liu,M. C.; Chen, X. G.; Hu, Z. D. Euro. J. Med. Chem. 2008, 43,569. doi: 10.1016/j.ejmech.2007.04.011
(13) Luan, F.; Liu, H. T.; Ma,W. P.; Fan, B. T. Euro. J. Med. Chem.2008, 43, 43. doi: 10.1016/j.ejmech.2007.03.002
(14) Melagraki, G.; Afantitis, A.; Sarimveis, H.; Koutentis, P. A.;Markopoulos, J.; Igglessi-Markopoulou, O. Bioorg. Med. Chem.2007, 15, 7237. doi: 10.1016/j.bmc.2007.08.036
(15) Su, L.; Li, L.; Li, Y.; Zhang, X.; Huang, X.; Zhai, H. Med. Chem. Res. 2012, 21, 2079. doi: 10.1007/s00044-011-9734-x
(16) de Vicente, J.; Hendricks, R. T.; Smith, D. B.; Fell, J. B.; Fischer,J.; Spencer, S. R.; Stengel, P. J.; Mohr, P.; Robinson, J. E.;Blake, J. F.; Hilgenkamp, R. K.; Yee, C.; Adjabeng, G.;Elworthy, T. R.; Li, J.;Wang, B.; Bamberg, J. T.; Harris, S. F.;Wong, A.; Leveque, V. J. P.; Najera, I.; Pogam, S. L.;Rajyaguru, S.; Ao-Ieong, G.; Alexandrova, L.; Larrabee, S.;Brandl, M.; Briggs, A.; Sukhtankar, S.; Farrell, R. Bioorg. Med. Chem. Lett. 2009, 19, 5652. doi: 10.1016/j.bmcl.2009.08.022
(17) Hendricks, R. T.; Spencer, S. R.; Blake, J. F.; Fell, J. B.; Fischer,J.; Stengel, P. J.; Leveque, V. J. P.; Pogam, S. L.; Rajyaguru, S.;Najera, I.; Swallow, S. Bioorg. Med. Chem. Lett. 2009, 19, 410.doi: 10.1016/j.bmcl.2008.11.060
(18) de Vicente, J.; Hendricks, R. T.; Smith, D. B.; Fell, J. B.; Fischer,J.; Spencer, S. R.; Stengel, P. J.; Mohr, P.; Robinson, J. E.;Blake, J. F.; Hilgenkamp, R. K.; Yee, C.; Adjabeng, G.;Elworthy, T. R.; Tracy, J.; Chin, E.; Li, J.;Wang, B.; Bamberg,J. T.; Stephenson, R.; Oshiro, C.; Harris, S. F.; Ghate, M.;Leveque, V.; Najera, I.; Pogam, S. L.; Rajyaguru, S.; Ao-Ieong,G.; Alexandrova, L.; Larrabee, S.; Brandl, M.; Briggs, A.;Sukhtankar, S.; Farrell, R.; Xu, B. Bioorg. Med. Chem. Lett.2009, 19, 3642. doi: 10.1016/j.bmcl.2009.05.004
(19) Hendricks, R. T.; Fell, J. B.; Blake, J. F.; Fischer, J. P.;Robinson, J. E.; Spencer, S. R.; Stengel, P. J.; Bernacki, A. L.;Leveque, V. J. P.; Pogam, S. L.; Rajyaguru, S.; Najera, I.; Josey,J. A.; Harris, J. R.; Swallow, S. Bioorg. Med. Chem. Lett. 2009,19, 3637. doi: 10.1016/j.bmcl.2009.04.119
(20) de Vicente, J.; Hendricks, R. T.; Smith, D. B.; Fell, J. B.; Fischer,J.; Spencer, S. R.; Stengel, P. J.; Mohr, P.; Robinson, J. E.;Blake, J. F.; Hilgenkamp, R. K.; Yee, C.; Zhao, J.; Elworthy, T.R.; Tracy, J.; Chin, E.; Li, J.; Lui, A.;Wang, B.; Oshiro, C.;Harris, S. F.; Ghate, M.; Leveque, V. J. P.; Najera, I.; Pogam, S.L.; Rajyaguru, S.; Ao-Ieong, G.; Alexandrova, L.; Fitch, B.;Brandl, M.; Masjedizadeh, M.;Wua, S. Y.; de Keczer, S.;Voronin, T. Bioorg. Med. Chem. Lett. 2009, 19, 5648.doi: 10.1016/j.bmcl.2009.08.023
(21) Todeschini, R.; Consonni, V. Handbook of Molecular Descriptors;Wiley-VCH: New York, 2000.
(22) Xue, Y.; Li, Z. R.; Yap, C.W.; Sun, L. Z.; Chen, X.; Chen, Y. Z.J. Chem. Inform. Comp. Sci. 2004, 44, 1630. doi: 10.1021/ci049869h
(23) Tan, N. X.; Rao, H. B.; Li, Z. R.; Li, X. Y. SAR QSAR Environ. Res. 2009, 20, 27. doi: 10.1080/10629360902724085
(24) http://www.models.kvl.dk/source/GAPLS/index.asp, accessedJune 2008.
(25) Leardi, R.; Boggia, R.; Terrile, M. J. Chemom. 1992, 6, 267.
(26) Leardi, R. J. Chemom. 1994, 8, 65.
(27) Burbidge, R.; Trotter, M.; Buxton, B.; Holden, S. Comput. Chem. 2001, 26, 5. doi: 10.1016/S0097-8485(01)00094-8
(28) Cherkassky, V.; Ma, Y. Selection of Meta-parameters forSupport Vector Regression. Proceedings of the InternationalConference on Artificial Neural Networks, Madrid, Spain, Aug28-30, 2002.
(29) Hao, M.; Li, Y.;Wang, Y.; Zhang, S. Anal. Chim. Acta 2011,690, 53. doi: 10.1016/j.aca.2011.02.004
(30) Rainville, F. M. D.; Fortin, F. A.; Gardner, M. A.; Parizeau, M.;Gagné, C. DEAP: A Python Framework for EvolutionaryAlgorithms. In EvoSoft Workshop, Companion Proc. of theGenetic and Evolutionary Computation Conference, July 07-11,2012.
(31) Chang, C. C.; Lin, C. J. LIBSVM: A Library for Support VectorMachines, 2001. Software available at http://www.csie.ntu.edu.tw/-cjlin/libsvm, accessed Jun 2008.

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Quantitative Structure-Activity Relationship Study of the Non-Nucleoside Inhibitors of HCV NS5B Polymerase by Machine Learning Methods

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