Comparative QSARs for antimalarial endochins: Importance of descriptor-thinning and noise reduction prior to feature selection

Chemometrics and Intelligent Laboratory Systems

Volumn 109, Issue 2, 2011, Pages 146-161

  Ojha, Probir Kumar ^a   Roy, Kunal ^a  

^a JADAVPUR UNIVERSITY   (India)

Author keywords

Antimalarial; OECD; QSAR; Validation

Indexed keywords

ANTIMALARIAL AGENT; ENDOCHIN DERIVATIVE; UNCLASSIFIED DRUG;

ARTICLE; CHEMOMETRIC ANALYSIS; CONTROLLED STUDY; DRUG STRUCTURE; FACTORIAL ANALYSIS; INTERMETHOD COMPARISON; MULTIPLE LINEAR REGRESSION ANALYSIS; NOISE REDUCTION; PARTIAL LEAST SQUARES REGRESSION; PRIORITY JOURNAL; PROCESS DEVELOPMENT; QUANTITATIVE STRUCTURE ACTIVITY RELATION; RELIABILITY; VALIDATION PROCESS;

EID: 80055096902     PISSN: 01697439     EISSN: 18733239     Source Type: Journal    
DOI: 10.1016/j.chemolab.2011.08.007     Document Type: Article

References (76)

1. World Malaria Report 2008, WHO, Geneva, Switzerland. http://www.who.int/entity/malaria/publications/atoz/%85/en/index.html.
2. Global burden of disease: 2004 update 2008, World Health Organization, Geneva.
3. Bathurst I., Hentschel C. Medicines for malaria venture: Sustaining antimalarial drug development. Trends in Parasitology 2006, 22:301-307.
4. Murray M.C., Perkins M.E. Chemotherapy of malaria. Annual Reports in Medicinal Chemistry 1996, 31:141-150.
5. White N.J., Olliaro P. Artemisinin and derivatives in the treatment of uncomplicated malaria. La Medicina Tropical 1998, 58:54-56.
6. Noedl H., Socheat D., Satimai W. Artemisinin-resistant malaria in Asia. The New England Journal of Medicine 2009, 361:540-541.
7. Rogers W.O., Sem R., Tero D., Chim P., Lim P., Muth S., Socheat D., Ariey F., Wongsrichanalai C. Failure of artesunate mefloquine combination therapy for uncomplicated Plasmodium falciparum malaria in Southern Cambodia. Malaria Journal 2009, 8:10.
8. Gonzalez-Diaz H., Cruz-Monteagudo M., Vina D., Santana L., Uriarte E., De Clercq E. QSAR for anti-RNA-virus activity, synthesis, and assay of anti-RSV carbonucleosides given a unified representation of spectral moments, quadratic, and topologic indices. Bioorganic & Medicinal Chemistry Letters 2005, 15:1651-1657.
9. Gonzales-Diaz H., Gia O., Uriarte E., Hernadez I., Ramos R., Chaviano M., Seijo S., Castillo J.A., Morales L., Santana L., Akpaloo D., Molina E., Cruz M., Torres L.A., Cabrera M.A. Markovian chemicals "in silico" design (MARCH-INSIDE), a promising approach for computer-aided molecular design I: discovery of anticancer compounds. Journal of Molecular Modeling 2003, 9:395-407.
10. Santana L., Uriarte E., Gonzalez-Diaz H., Zagotto G., Soto-Otero R., Mendez-Alvarez E. A QSAR model for in silico screening of MAO-A inhibitors. Prediction, synthesis, and biological assay of novel coumarins. Journal of Medicinal Chemistry 2006, 49:1149-1156.
11. Du Q.S., Mezey P.G., Chou K.C. Heuristic molecular lipophilicity potential (HMLP): A 2D-QSAR study to LADH of molecular family pyrazole and derivatives. Journal of Computational Chemistry 2005, 26:461-470.
12. Du Q.S., Huang R.B., Wei Y.T., Du L.Q., Chou K.C. Multiple field three dimensional quantitative structure-activity relationship (MF-3D-QSAR). Journal of Computational Chemistry 2008, 29:211-219.
13. Du Q.S., Huang R.B., Chou K.C. Recent advances in QSAR and their applications in predicting the activities of chemical molecules, peptides and proteins for drug design. Current Protein & Peptide Science 2008, 9:248-259.
14. He Z., Zhang J., Shi X.H., Hu L.L., Kong X., Cai Y.D., Chou K.C. Predicting drug-target interaction networks based on functional groups and biological features. PloS One 2010, 5:e9603.
15. Chou K.C. A vectorized sequence-coupling model for predicting HIV protease cleavage sites in proteins. The Journal of Biological Chemistry 1993, 268:16938-16948.
16. Wang P., Hu L., Liu G., Jiang N., Chen X., Xu J., Zheng W., Li L., Tan M., Chen Z., Song H., Cai Y.D., Chou K.C. Prediction of antimicrobial peptides based on sequence alignment and feature selection methods. PloS One 2011, 6:e18476.
17. Roy K., Ojha P.K. Advances in quantitative structure-activity relationship models of antimalarials. Expert Opinion on Drug Discovery 2010, 5:751-778.
18. Gupta M.K., Prabhakar Y.S. Topological descriptors in modeling the antimalarial activity of 4-(3',5'-disubstituted anilino)quinolines. Journal of Chemical Information and Modeling 2006, 46:93-102.
19. Agrawal V.K., Srivastava R., Khadikar P.V. QSAR studies on some antimalarial sulfonamides. Bioorganic & Medicinal Chemistry 2001, 93:287-3293.
20. Ojha P.K., Roy K. Chemometric modeling, docking and in silico design of triazolopyrimidine-based dihydroorotate dehydrogenase inhibitors as antimalarials. European Journal of Medicinal Chemistry 2010, 45:4645-4656.
21. Ojha P.K., Roy K. Exploring molecular docking and QSAR studies of plasmepsin-II inhibitor di-tertiary amines as potential antimalarial compounds. Molecular Simulation 2011, 37:779-803.
22. Ojha P.K., Roy K. Chemometric modelling of antimalarial activity of aryltriazolylhydroxamates. Molecular Simulation 2010, 36:939-952.
23. Ojha P.K., Roy K. Exploring QSAR, pharmacophore mapping and docking studies and virtual library generation for cycloguanil derivatives as PfDHFR-TS inhibitors. Medicinal Chemistry 2011, 7:173-199.
24. Salzer W., Timmler H., Andersag H. A new type of compounds active against avian malaria. Chemische Berichte 1948, 81:12-19.
25. Winter R.W., Kelly J.X., Smilkstein M.J., Dodean R., Hinrichs D., Riscoe M.K. Antimalarial quinolones: synthesis, potency, and mechanistic studies. Experimental Parasitology 2008, 118:487-497.
26. Shen Q., Jiang J.H., Shen G., Yu R.Q. Variable selection by an evolution algorithm using modified Cp based on MLR and PLS modeling: QSAR studies of carcinogenicity of aromatic amines. Analytical and Bioanalytical Chemistry 2003, 375:248-254.
27. Bayram E., Santago P., Harris R., Xiao Y.D., Clauset A.J., Schmitt J.D. Genetic algorithms and self-organizing maps: a powerful combination for modeling complex QSAR and QSPR problems. Journal of Computer-Aided Molecular Design 2004, 18:483-493.
28. Schefzick S., Bradley M. Comparison of commercially available genetic algorithms: GAs as variable selection tool. Journal of Computer-Aided Molecular Design 2004, 18:511-521.
29. Zheng W., Tropsha A. Novel variable selection quantitative structure - property relationship approach based on the k-nearest-neighbor principle. Journal of Chemical Information and Computer Sciences 2000, 40:185-194.
30. Ghafourian T., Cronin M. The effect of variable selection on the non-linear modeling of oestrogen receptor binding. QSAR and Combinatorial Science 2006, 25:824-835.
31. Chou K.C. Some remarks on protein attribute prediction and pseudo amino acid composition. Journal of Theoretical Biology 2011, 273:236-247.
32. Cross R.M., Monastyrskyi A., Mutka T.S., Burrows J.N., Kyle D.E., Manetsch R. Endochin optimization: Structure-activity and structure-property relationship studies of 3-substituted 2-Methyl-4(1H)-quinolones with antimalarial activity. Journal of Medicinal Chemistry 2010, 53:7076-7094.
33. Desjardins R.E., Canfield C.J., Haynes J.D., Chulay J.D. Quantitative assessment of antimalarial activity in vitro by a semiautomated microdilution technique. Antimicrobial Agents and Chemotherapy 1979, 16:710-718.
34. Kubinyi H. The quantitative analysis of structure-activity relationships. Burger's Medicinal Chemistry and Drug Discovery 1995, 494-571. John Wiley. M.E. Wolff (Ed.).
35. Selassie C.D. History of Quantitative Structure-Activity Relationships. Burger's Medicinal Chemistry and Drug Discovery 2003, 1-48. Wiley. J. Abraham (Ed.).
36. Cerius2 Version 4.10 is a product of Accelrys Inc., San Diego, CA, 2005.
37. SPSS is statistical software of SPSS Inc., USA, 1999.
38. Leonard J.T., Roy K. On selection of training and test sets for the development of predictive QSAR models. QSAR and Combinatorial Science 2006, 25:235-251.
39. Stephens M.A. Asymptotic Results for Goodness of Fit Statistics with Unknown Parameters. The Annals of Statistics 1976, 4:357-369.
40. Massey F.J. The Kolmogorov-Smirnov test for goodness of fit. Journal of the American Statistical Association 1951, 46:68-78.
41. Darlington R.B. In Regression and linear models 1990, New York, McGraw- Hill.
42. Wold S., Sjostrom M., Eriksson L. PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems 2001, 58:109-130.
43. Fan Y., Shi L.M., Kohn K.W., Pommier Y., Weinstein J.N. Quantitative structure-antitumor activity relationships of camptothecin analogues: cluster analysis and genetic algorithm-based studies. Journal of Medicinal Chemistry 2001, 44:3254-3263.
44. Wold S. Chemometric Methods in Molecular Design 1995, 195. VCH, Weinheim. H. van de Waterbeemd (Ed.).
45. Franke R. Theoretical Drug Design Methods 1984, 184. Elsevier, Amsterdam.
46. Franke R., Gruska A. Chemometric Methods in Molecular Design 1995, 113-163. VCH, Weinheim. H. van de Waterbeemd (Ed.).
47. Rogers D., Hopfinger A.J. Application of genetic function approximation to quantitative structure-activity relationships and quantitative structure-property relationships. Journal of Chemical Information and Computer Sciences 1994, 34:854-866.
48. Holland J. Adaptation in Artificial and Natural Systems 1975, University of Michigan Press, Ann Arbor, MI.
49. Friedman J. Multivariate Adaptive Regression Splines. Technical Report No. 102, Laboratory for Computational Statistics, Department of Statistics Nov 1988, Stanford University, Stanford, CA, (revised Aug 1990).
50. Martens H., Naes T. Multivariate Calibration 1989, Wiley, Chichester, etc.
51. Höskuldsson A. PLS regression methods. Journal of Chemometrics 1988, 2:211-228.
52. Eriksson L., Johansson E., Kettaneh-Wold N., Wold S. Multi- and megavariate data analysis: Principles and applications 2001, Umetrics, Umeå.
53. Mitra I., Saha A., Roy K. Exploring quantitative structure-activity relationship studies of antioxidant phenolic compounds obtained from traditional Chinese medicinal plants. Molecular Simulation 2010, 36:1067-1079.
54. Héberger K. Sum of ranking differences compares methods or models fairly. Trends in Analytical Chemistry 2010, 29:101-109.
55. Héberger K., Kollár-Hunek K. Sum of ranking differences for method discrimination and its validation: comparison of ranks with random numbers. Journal of Chemometrics 2010, 25:151-158.
56. O'Brien M.Robert A caution regarding rules of thumb for variance inflation factors. Quality and Quantity 2007, 41:673-690.
57. Snedecor G.W., Cochran W.G. Statistical Methods 1967, Oxford & IBH, New Delhi.
58. Kubinyi H., Hamprecht F.A., Mietzner T. Three-dimensional quantitative structure-activity relationships (3D QSiAR) from SEAL similarity matrices. Journal of Medicinal Chemistry 1998, 41:2553-2564.
59. Golbraikh A., Tropsha A. Beware of q2!. Journal of Molecular Graphics & Modelling 2002, 20:269-276.
60. Chou K.C., Shen H.B. Cell-PLoc: A package of Web servers for predicting subcellular localization of proteins in various organisms. Nature Protocols 2008, 3:153-162.
61. Chou K.C., Shen H.B. Review: Recent progresses in protein subcellular location prediction. Analytical Biochemistry 2007, 370:1-16.
62. Marshall G.R. Binding-site modeling of unknown receptors. 3D QSAR in Drug Design - Theory, Methods and Applications 1994, 80-116. Leiden, ESCOM. H. Kubinyi (Ed.).
63. Consonni V., Ballabio D., Todeschini R. Comments on the definition of the Q2 parameter for QSAR validation. Journal of Chemical Information and Modeling 2009, 49:1669-1678.
64. Roy P.P., Roy K. On some aspects of variable selection for partial least squares regression models. QSAR and Combinatorial Science 2008, 27:302-313.
65. Roy P.P., Paul S., Mitra I., Roy K. On two novel parameters for validation of predictive QSAR models. Molecules 2009, 14:1660-1701.
66. Mitra I., Roy P.P., Kar S., Ojha P.K., Roy K. On further application of rm2 as a metric for validation of QSAR models. Journal of Chemometrics 2010, 24:22-33.
67. Ojha P.K., Mitra I., Das R.N., Roy K. Further exploring rm2 metrics for validation of QSPR models. Chemometrics and Intelligent Laboratory Systems 2011, 107:194-205.
68. Roy K., Paul S. Exploring 2D and 3D QSARs of 2,4-diphenyl-1,3-oxazolines for ovicidal activity against tetranychus urticae. QSAR and Combinatorial Science 2009, 28:406-425.
69. Roy K., Paul S. Docking and 3D-QSAR studies of acetohydroxy acid synthase inhibitor sulfonylurea derivatives. Journal of Molecular Modeling 2010, 16:951-964.
70. Ghose A.K., Pritchett A., Crippen G.M. Atomic Physicochemical Parameters for Three-Dimensional Structure-Directed Quantitative Structure-Activity Relationships 3. Journal of Computational Chemistry 1988, 9:80-90.
71. http://www.oecd.org/document/23/0,3746,en_2649_34379_33957015_1_1_1_1,00.html.
72. http://www.oecd.org/dataoecd/33/37/37849783.pdf.
73. STATISTICA is a statistical software of STATSOFT Inc., USA http://www.statsoft.com/.
74. Gramatica P. Principles of QSAR models validation: internal and external. QSAR and Combinatorial Science 2007, 26:694-701.
75. Eriksson L., Jaworska J., Worth A.P., Cronin M.T., McDowell R.M., Gramatica P. Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs. Environmental Health Perspectives 2003, 111:1361-1375.
76. UMETRICS SIMCA-P 10.0 Umea, Sweden. http://info@umetrics.com:www.umetrics.com.