Simplified molecular input line entry system-based descriptors in QSAR modeling for HIV-protease inhibitors

Chemometrics and Intelligent Laboratory Systems

Volumn 153, Issue , 2016, Pages 67-74

  Islam, Md Ataul ^a   Pillay, Tahir S ^a,b  

^a UNIVERSITY OF PRETORIA   (South Africa)

^b UNIVERSITY OF CAPE TOWN   (South Africa)

Author keywords

CORAL software; Descriptors; HIV protease; Monte Carlo method; QSAR; SMILES

Indexed keywords

ATAZANAVIR; HUMAN IMMUNODEFICIENCY VIRUS PROTEINASE INHIBITOR; INDINAVIR; NELFINAVIR; SAQUINAVIR;

ARTICLE; CALIBRATION; COMPUTER PROGRAM; CONTROLLED STUDY; DRUG STRUCTURE; ENZYME INHIBITION; MACHINE LEARNING; MOLECULAR MODEL; MONTE CARLO METHOD; PRIORITY JOURNAL; QUALITY CONTROL; QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP; SIMPLIFIED MOLECULAR INPUT LINE ENTRY SYSTEM; STRUCTURE ACTIVITY RELATION; VALIDATION STUDY;

EID: 84960372083     PISSN: 01697439     EISSN: 18733239     Source Type: Journal    
DOI: 10.1016/j.chemolab.2016.02.008     Document Type: Article

References (54)

1. Gallo R.C., Sarin P.S., Gelmann E.P., Robert-Guroff M., Richardson E., Kalyanaraman V.S., Mann D., Sidhu G.D., Stahl R.E., Zolla-Pazner S., Leibowitch J., Popovic M. Isolation of human T-cell leukemia virus in acquired immune deficiency syndrome (AIDS). Science 1983, 220:865-867.
2. Chin'ombe N., Ruhanya V. HIV/AIDS vaccines for Africa: scientific opportunities, challenges and strategies. Pan Afr. Med. J. 2015, 20:386.
3. Barre-Sinoussi F., Chermann J.C., Rey F., Nugeyre M.T., Chamaret S., Gruest J., Dauguet C., Axler-Blin C., Vezinet-Brun F., Rouzioux C., Rozenbaum W., Montagnier L. Isolation of a T-lymphotropic retrovirus from a patient at risk for acquired immune deficiency syndrome (AIDS). Science 1983, 220:868-871.
4. Popovic M., Sarngadharan M.G., Read E., Gallo R.C. Detection, isolation, and continuous production of cytopathic retroviruses (HTLV-III) from patients with AIDS and pre-AIDS. Science 1984, 224:497-500.
5. Sarngadharan M.G., Popovic M., Bruch L., Schupbach J., Gallo R.C. Antibodies reactive with human T-lymphotropic retroviruses (HTLV-III) in the serum of patients with AIDS. Science 1984, 224:506-508.
6. Hemelaar J., Gouws E., Ghys P.D., Osmanov S. Global and regional distribution of HIV-1 genetic subtypes and recombinants in 2004. AIDS 2006, 20:W13-W23.
7. Wlodawer A., Miller M., Jaskolski M., Sathyanarayana B.K., Baldwin E., Weber I.T., Selk L.M., Clawson L., Schneider J., Kent S.B. Conserved folding in retroviral proteases: crystal structure of a synthetic HIV-1 protease. Science 1989, 245:616-621.
8. Bartlett J.A., DeMasi R., Quinn J., Moxham C., Rousseau F. Overview of the effectiveness of triple combination therapy in antiretroviral-naive HIV-1 infected adults. AIDS 2001, 15:1369-1377.
9. Gulick R.M., Mellors J.W., Havlir D., Eron J.J., Meibohm A., Condra J.H., Valentine F.T., McMahon D., Gonzalez C., Jonas L., Emini E.A., Chodakewitz J.A., Isaacs R., Richman D.D. 3-year suppression of HIV viremia with indinavir, zidovudine, and lamivudine. Ann. Intern. Med. 2000, 133:35-39.
10. Palella F.J., Delaney K.M., Moorman A.C., Loveless M.O., Fuhrer J., Satten G.A., Aschman D.J., Holmberg S.D. Declining morbidity and mortality among patients with advanced human immunodeficiency virus infection. HIV outpatient study investigators. N. Engl. J. Med. 1998, 338:853-860.
11. Hogg R.S., Heath K.V., Yip B., Craib K.J., O'Shaughnessy M.V., Schechter M.T., Montaner J.S. Improved survival among HIV-infected individuals following initiation of antiretroviral therapy. JAMA 1998, 279:450-454.
12. Waters L., Nelson M. Why do patients fail HIV therapy?. Int. J. Clin. Pract. 2007, 61:983-990.
13. Condra J.H., Schleif W.A., Blahy O.M., Gabryelski L.J., Graham D.J., Quintero J.C., Rhodes A., Robbins H.L., Roth E., Shivaprakash M., et al. In vivo emergence of HIV-1 variants resistant to multiple protease inhibitors. Nature 1995, 374:569-571.
14. Clavel F., Hance A.J. HIV drug resistance. N. Engl. J. Med. 2004, 350:1023-1035.
15. Tomasselli A.G., Heinrikson R.L. Targeting the HIV-protease in AIDS therapy: a current clinical perspective. Biochim. Biophys. Acta 2000, 1477:189-214.
16. Reddy G.S., Ali A., Nalam M.N., Anjum S.G., Cao H., Nathans R.S., Schiffer C.A., Rana T.M. Design and synthesis of HIV-1 protease inhibitors incorporating oxazolidinones as P2/P2' ligands in pseudosymmetric dipeptide isosteres. J. Med. Chem. 2007, 50:4316-4328.
17. Ghosh A.K., Chapsal B.D., Weber I.T., Mitsuya H. Design of HIV protease inhibitors targeting protein backbone: an effective strategy for combating drug resistance. Acc. Chem. Res. 2008, 41:78-86.
18. Wensing A.M., van Maarseveen N.M., Nijhuis M. Fifteen years of HIV protease inhibitors: raising the barrier to resistance. Antivir. Res. 2010, 85:59-74.
19. Ghosh A.K., Chapsal B.D., Baldridge A., Steffey M.P., Walters D.E., Koh Y., Amano M., Mitsuya H. Design and synthesis of potent HIV-1 protease inhibitors incorporating hexahydrofuropyranol-derived high affinity P(2) ligands: structure-activity studies and biological evaluation. J. Med. Chem. 2011, 54:622-634.
20. Ganguly A.K., Alluri S.S., Caroccia D., Biswas D., Wang C.H., Kang E., Zhang Y., McPhail A.T., Carroll S.S., Burlein C., Munshi V., Orth P., Strickland C. Design, synthesis, and X-ray crystallographic analysis of a novel class of HIV-1 protease inhibitors. J. Med. Chem. 2011, 54:7176-7183.
21. Qiu X., Zhao G.D., Tang L.Q., Liu Z.P. Design and synthesis of highly potent HIV-1 protease inhibitors with novel isosorbide-derived P2 ligands. Bioorg. Med. Chem. Lett. 2014, 24:2465-2468.
22. Zivkovic J.V., Trutic N.V., Veselinovic J.B., Nikolic G.M., Veselinovic A.M. Monte Carlo method based QSAR modeling of maleimide derivatives as glycogen synthase kinase-3beta inhibitors. Comput. Biol. Med. 2015, 64:276-282.
23. K. Roy, S. Kar, R.N. Das, A primer on QSAR/QSPR modeling fundamental concepts, Springer International Publishing 2015.
24. Duchowicz P.R., Comelli N.C., Ortiz E.V., Castro E.A. QSAR study for carcinogenicity in a large set of organic compounds. Current Drug Safety 2012, 7:282-288.
25. Talevi A., Bellera C.L., Di Ianni M., Duchowicz P.R., Bruno-Blanch L.E., Castro E.A. An integrated drug development approach applying topological descriptors. Curr. Comput.-Aided Drug Des. 2012, 8:172-181.
26. Randic M., Basak S.C. A new descriptor for structure-property and structure-activity correlations. J. Chem. Inf. Comput. Sci. 2001, 41:650-656.
27. Junkes Bda S., Arruda A.C., Yunes R.A., Porto L.C., Heinzen V.E. Semi-empirical topological index: a tool for QSPR/QSAR studies. J. Mol. Model. 2005, 11:128-134.
28. Ivanciuc O. Chemical graphs, molecular matrices and topological indices in chemoinformatics and quantitative structure-activity relationships. Curr. Comput.-Aided Drug Des. 2013, 9:153-163.
29. Weininger D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Model. 1988, 28:31-36.
30. Weininger D., Weininger A., Weininger J.L. SMILES. 2. Algorithm for generation of unique SMILES notation. J. Chem. Inf. Model. 1989, 29:97-101.
31. Weininger D. SMILES. 3. Depict. Graphical depiction of chemical structures. J. Chem. Inf. Model. 1990, 30:237-243.
32. Toropov A.A., Benfenati E. SMILES in QSPR/QSAR modeling: results and perspectives. Curr. Drug Discovery Technol. 2007, 4:77-116.
33. Toropov A.A., Benfenati E. SMILES as an alternative to the graph in QSAR modelling of bee toxicity. Comput. Biol. Chem. 2007, 31:57-60.
34. Veselinovic A.M., Veselinovic J.B., Zivkovic J.V., Nikolic G.M. Application of SMILES notation based optimal descriptors in drug discovery and design. Curr. Top. Med. Chem. 2015, 15:1768-1779.
35. Toropov A.A., Toropova A.P., Diaza R.G., Benfenati E., Gini G. SMILES-based optimal descriptors: QSAR modeling of estrogen receptor binding affinity by correlation balance. Struct. Chem. 2012, 23:529-544.
36. Veselinovic A.M., Milosavljevic J.B., Toropov A.A., Nikolic G.M. SMILES-based QSAR models for the calcium channel-antagonistic effect of 1,4-dihydropyridines. Arch. Pharm. 2013, 346:134-139.
37. Veselinovic A.M., Milosavljevic J.B., Toropov A.A., Nikolic G.M. SMILES-based QSAR model for arylpiperazines as high-affinity 5-HT(1A) receptor ligands using CORAL. Eur. J. Pharm. Sci. 2013, 48:532-541.
38. Veselinovic J.B., Toropov A.A., Toropova A.P., Nikolic G.M., Veselinovic A.M. Monte carlo method-based QSAR modeling of penicillins binding to human serum proteins. Arch. Pharm. 2015, 348:62-67.
39. Worachartcheewan A., Mandi P., Prachayasittikul V., Toropova A.P., Toropov A.A., Nantasenamat C. Large-scale QSAR study of aromatase inhibitors using SMILES-based descriptors. Chemom. Intell. Lab. Syst. 2014, 138:120-126.
40. Li Q., Ding X., Si H., Gao H. QSAR model based on SMILES of inhibitory rate of 2,3-diarylpropenoic acids on AKR1C3. Chemom. Intell. Lab. Syst. 2014, 139:120-126.
41. Toropova A.P., Toropov A.A., Veselinovic J.B., Miljkovic F.N., Veselinovic A.M. QSAR models for HEPT derivates as NNRTI inhibitors based on Monte Carlo method. Eur. J. Med. Chem. 2014, 77:298-305.
42. Ali A., Reddy G.S., Cao H., Anjum S.G., Nalam M.N., Schiffer C.A., Rana T.M. Discovery of HIV-1 protease inhibitors with picomolar affinities incorporating N-aryl-oxazolidinone-5-carboxamides as novel P2 ligands. J. Med. Chem. 2006, 49:7342-7356.
43. Ali A., Reddy G.S., Nalam M.N., Anjum S.G., Cao H., Schiffer C.A., Rana T.M. Structure-based design, synthesis, and structure-activity relationship studies of HIV-1 protease inhibitors incorporating phenyloxazolidinones. J. Med. Chem. 2010, 53:7699-7708.
44. Parai M.K., Huggins D.J., Cao H., Nalam M.N., Ali A., Schiffer C.A., Tidor B., Rana T.M. Design, synthesis, and biological and structural evaluations of novel HIV-1 protease inhibitors to combat drug resistance. J. Med. Chem. 2012, 55:6328-6341.
45. Toropova M.A., Toropov A.A., Raska I., Raskova M. Searching therapeutic agents for treatment of Alzheimer disease using the Monte Carlo method. Comput. Biol. Med. 2015, 64:148-154.
46. Kubinyi H., Hamprecht F.A., Mietzner T. Three-dimensional quantitative similarity-activity relationships (3D QSiAR) from SEAL similarity matrices. J. Med. Chem. 1998, 41:2553-2564.
47. Roy K., Mitra I., Kar S., Ojha P.K., Das R.N., Kabir H. Comparative studies on some metrics for external validation of QSPR models. J. Chem. Inf. Model. 2012, 52:396-408.
48. Ojha P.K., Mitra I., Das R.N., Roy K. Further exploring rm2 metrics for validation of QSPR models. Chemom. Intell. Lab. Syst. 2011, 107:194-205.
49. Pratim Roy P., Paul S., Mitra I., Roy K. On two novel parameters for validation of predictive QSAR models. Molecules 2009, 14:1660-1701.
50. Roy P.P., Roy K. On some aspects of variable selection for partial least squares regression models. Mol. Inf. 2007, 27:302-313.
51. Ojha P.K., Roy R. Comparative QSARs for antimalarial endochins: importance of descriptor-thinning and noise reduction prior to feature selection. Chemom. Intell. Lab. Syst. 2011, 109:146-161.
52. OECD Guidance Document on the Validation of (Quantitative) Structure-Activity Relationships [(Q) SAR] Models, ENV/JM/MONO(2007(2) 2007, Organisation for Economic Co-operation and Development, Paris.
53. Tropsha A., Gramatica P., Gombar V.K. The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models, 22 (2003) 69-77. QSAR Comb. Sci. 2003, 22:69-77.
54. Ojha P.K., Mitra I., Das R.N., Roy K. Further exploring r m 2 metrics for validation of QSPR models. Chemom. Intell. Lab. Syst. 2011, 107:194-205.