The receptor-dependent QSAR paradigm: An overview of the current state of the art

Medicinal Chemistry

Volumn 5, Issue 4, 2009, Pages 359-366

  Santos Filho, Osvaldo A ^a   Hopfinger, Anton J ^b,c   Cherkasov, Artem ^d   De Alencastro, Ricardo Bicca ^a  

^a FEDERAL UNIVERSITY OF RIO DE JANEIRO   (Brazil)

^b UNIVERSITY OF NEW MEXICO   (United States)

^c Chem21 Group Inc   (United States)

^d UNIVERSITY OF BRITISH COLUMBIA   (Canada)

Author keywords

3D QSAR; Drug design; QSAR; Receptor dependent QSAR; Structure based QSAR

Indexed keywords

RECEPTOR;

ALGORITHM; COMPUTER; COMPUTER MODEL; COMPUTER PROGRAM; DATA ANALYSIS; DRUG DESIGN; MOLECULAR MODEL; PRIORITY JOURNAL; QUANTITATIVE STRUCTURE ACTIVITY RELATION; REVIEW;

EID: 70249128671     PISSN: 15734064     EISSN: None     Source Type: Journal    
DOI: 10.2174/157340609788681458     Document Type: Review

References (68)

1. Tollenaere, J. P. In Guidebook on Molecular Modeling in Drug Design; Cohen, N. C., Ed.; Academic Press: San Diego, 1996, pp. 337-356.
2. Hansch, C.; Fujita, T. ρ-σ-π analysis. A method for correlation of biological activity and chemical structure. J. Am. Chem. Soc., 1964, 86, 1616-1626.
3. Free, S. M., Jr.; Wilson, J. W. A mathematical contribution to structure-activity studies. J. Med. Chem., 1964, 7, 395-399.
4. Esposito, E. X.; Hopfinger, A. J.; Madura, J. D. In Methods in Molecular Biology: Chemoinformatics: Concepts, Methods, and Tools for Drug Discovery. Bajorath, J., Ed.; Humana Press: Totowa, 2004, Vol 275, pp. 131-213.
5. Tropsha, A. In Comprehension Medicinal Chemistry II; Mason, J., Ed.; Elsevier: New York, 1997; Vol. 4, pp. 149-165.
6. Doweyko, A. In Comprehension Medicinal Chemistry II; Mason, J., Ed.; Elsevier: New York, 1997; Vol. 4, pp. 575-594.
7. Terfloth, L. In Chemoinformatics: A Textbook, Gasteiger, J.; Engel, T., Eds.; Wiley-VCH: Weinheim, 2003, pp. 401-437.
8. Hopfinger, A. J. A QSAR investigation of dihydrofolate reductase inhibition by Baker triazines based upon molecular shape analysis. J. Am. Chem. Soc., 1980, 102, 7196-7206.
9. Hopfinger, A. J. Inhibition of dihydrofolate reductase: structure-activity correlations of 2,4-diamino-5-benzylpyrimidines based upon molecular shape analysis. J. Med. Chem., 1981, 24, 818-822.
10. Hopfinger, A. J.; Wang, S.; Tokarski, J. S.; Jin, B.; Albuquerque, M.; Madhav, P. J.; Duraiswami, C. Construction of 3D-QSAR models using the 4D-QSAR analysis formalism. J. Am. Chem. Soc., 1997, 119, 10509-10524.
11. 2 receptor antagonists. J. Chem. Inf. Comput. Sci., 1998, 38, 925-938.
12. Prabha, V. P.; Hopfinger, A. J. Prediction of ligand - receptor binding free energy by 4D-QSAR analysis: application to a set of glucose analogue inhibitors of glycogen fhosphorylase, J. Chem. Inf. Comput. Sci., 1999, 39, 1141-115.
13. Santos-Filho, O. A; Hopfinger, A. J. A search for sources of drug resistance by the 4D-QSAR analysis of a set of antimalarial dihydrofolate reductase inhibitors. J. Comput.-Aided Mol. Des., 2001, 15, 1-12.
14. Duca, J. S.; Hopfinger, A. J. Estimation of molecular similarity based on 4D-QSAR analysis: formalism and validation. J. Chem. Inf. Comput. Sci., 2001, 41, 1367-1387.
15. Ravi, M.; Hopfinger, A. J.; Hormann, R. E.; Dinan, L. 4D-QSAR analysis of a set of ecdysteroids and a comparison to CoMFA modeling. J. Chem. Inf. Comput. Sci., 2001, 41, 1587-1604.
16. A receptor. J. Med. Chem., 2002, 45, 3210-3221.
17. A receptor site using 4D-QSAR analysis. J. Chem. Inf. Comput. Sci., 2003, 43, 324-336.
18. Senese, C. L.; Hopfinger, A. J. Receptor-independent 4D-QSAR analysis of a set of norstatine derived inhibitors of HIV-1 protease. J. Chem. Inf. Comput. Sci., 2003, 43, 1297-1307.
19. Liu, J.; Pan, D.; Tseng, Y.; Hopfinger, A. J. 4D-QSAR analysis of a series of antifungal P450 inhibitors and 3D-pharmacophore comparisons as a function of alignment. J. Chem. Inf. Comput. Sci., 2003, 43, 2170-2179.
20. Senese, C. L.; Hopfinger, A. J. A simple clustering technique to improve QSAR model selection and predictivity: application to a receptor independent 4D-QSAR analysis of cyclic urea derived inhibitors of HIV-1 protease. J. Chem. Inf. Comput. Sci., 2003, 43, 2180-2193.
21. Pasqualoto, K. F. M.; Ferreira, E. I.; Santos-Filho, O. A.; Hopfinger, A. J. Rational design of new antituberculosis agents: receptor-independent four-dimensional quantitative structure - activity relationship analysis of a set of isoniazid derivatives. J. Med. Chem., 2004, 47, 3755-3764.
22. Pan, D.; Tseng, Y.; Hopfinger, A. J. Quantitative structure-based design: formalism and application of receptor-dependent RD-4D-QSAR analysis to a set of glucose analogue inhibitors of glycogen phosphorylase. J. Chem. Inf. Comput. Sci., 2003, 43, 1591-1607.
23. Pan, D.; Liu, J.; Senese, C.; Hopfinger, A. J.; Tseng, Y. Characterization of a ligand-receptor binding event using receptor-dependent four-dimensional quantitative structure-activity relationship analysis. J. Med. Chem., 2004, 47, 3075-3088.
24. Santos-Filho, O. A.; Hopfinger, A. J. Structure-based QSAR analysis of a set of 4-hydroxy-5,6-dihydropyrones as inhibitors of HIV-1 protease: an application of the receptor-dependent (RD) 4D-QSAR formalism. J. Chem. Inf. Model., 2006, 46, 345-354.
25. Biographics Laboratory 3R. Friedensgasse 35, CH-4056 Basel, Switzerland.
26. Vedani, A.; Briem, H.; Dobler, M.; Dollinger, K.; McMasters, D. R. Multiple conformation and protonation-state representation in 4D-QSAR: the neurokinin-1 receptor system. J. Med. Chem., 2000, 43, 4416-4427.
27. Vedani, A.; Dobler, M. Multidimensional QSAR in drug research: predicting binding affinities, toxicity, and pharmacokinetic parameters. In Progress in Drug Research; Jucker, E., Ed.; Birkhäuser: Basel/Boston/Berlin, 2000; pp. 105-135.
28. Vedani, A.; McMasters, D. R.; Dobler, M. Multi-conformational ligand representation in 4D-QSAR: Reducing the bias associated with ligand alignment. Quant. Struct.-Act. Relat., 2000, 19, 149-161.
29. Streich, D.; Neuburger-Zehnder, M.; Vedani, A. Induced fit-the key for understanding LSD activity. A 4D-QSAR study on the 5-HT2A receptor system. Quant. Struct.-Act. Relat., 2000, 19, 565-573.
30. Vedani, A.; Dobler, M. 5D-QSAR: the key for simulating induced fit? J. Med. Chem., 2002, 45, 2139-2149.
31. Vedani, A.; Dobler, M.; Lill, M. A. Combining protein modeling and 4D-QSAR. Simulating the binding of structurally diverse ligands to the estrogen receptor. J. Med. Chem., 2005, 48, 3700-3703.
32. Tokarski, J. S; Hopfinger, A. J. Prediction of ligand-receptor binding thermodynamics by free energy force field (FEFF) 3D-QSAR analysis: application to a set of peptidometic renin inhibitors. J. Chem. Inf. Model., 1997, 37, 792-811.
33. Hopfinger, A. J. In Conformational Properties of Macromolecules; Academic Press: New York, 1973; p. 71.
34. Forsythe, K. H.; Hopfinger, A. J. The influence of solvent on the secondary structures of poly(L-alanine) and poly (L-proline). Macromolecules, 1973, 6, 423-437.
35. Venkataragan, P.; Hopfinger, A. J. Prediction of ligand-receptor binding thermodynamics by free energy force field three-dimensional quantitative structure-activity relationship analysis: application to a set of glucose analogue inhibitors of glycogen phosphorylase. J. Med. Chem, 1999, 42, 2169-2179.
36. Santos-Filho, O. A.; Hopfinger, A. J. Free energy force field (FEFF) 3D-QSAR analysis of a set of Plasmodium falciparum dihydrofolate reductase inhibitors. J. Comput. Aided Mol. Des., 2001, 15, 787-810.
37. Kulkarni, A. S.; Hopfinger, A. J.; Osborne, R.; Bruner, L. H.; Thompson, E. D. Prediction of eye irritation from organic chemicals using membrane-interaction QSAR analysis. Toxicol. Sci., 2001, 59, 335-345.
38. Kulkarni, A. S.; Hopfinger, A. J. Membrane-interaction QSAR analysis: application to the estimation of eye irritation by organic compounds. Pharm. Res., 1999, 16, 1244-1252.
39. Kulkarni, A. S.; Han, Y.; Hopfinger, A. J. Predicting Caco-2 cell permeation coefficients of organic molecules using membrane-interaction QSAR analysis. J. Chem. Inf. Comput. Sci., 2002, 42, 331-342.
40. Santos-Filho, O. A.; Hopfinger, A. J. Combined 4D-fingerprint and cluster based membrane-interaction QSAR analysis for constructing consensus Caco-2 cell permeation virtual screens. J. Pharm. Sci., 2008, 97, 566-583.
41. Iyer, M.; Mishra, R.; Han, Y.; Hopfinger, A. J. Predicting blood-brain barrier partitioning of organic molecules using membrane-interaction QSAR analysis. Pharm. Res., 2002, 19, 1611-1621.
42. Santos-Filho, O. A.; Hopfinger, A. J.; Zheng, T. Characterization of skin penetration processes of organic molecules using molecular similarity and QSAR analysis. Mol. Pharm., 2004, 1, 466-476.
43. Liu, J.; Li, Y.; Pan, D.; Hopfinger, A. J. Predicting permeability coefficient in ADMET evaluation by using different membranes-interaction QSAR. Int. J. Pharm., 2005, 304, 115-123.
44. Ortiz, A. R.; Pisabarro, M. T.; Gago, F.; Wade, R. C. Prediction of drug binding affinities by comparative binding energy analysis. J. Med. Chem., 1995, 38, 2681-2691.
45. Kmunicek, J.; Luengo, S.; Gago, F.; Ortiz, A. R.; Wade, R. C.; Damborsky, J. Comparative binding energy analysis of the substrate specificity of haloalkane dehalogenase from Xanthobacter autotrophicus GJ10. Biochemistry, 2001, 40, 8905-8917.
46. Wang, T.; Wade, R. C. Comparative binding energy (COMBINE) analysis of OppA-peptide complexes to relate structure to binding thermodynamics. J. Med. Chem., 2002, 45, 4828-4837.
47. Wang, T.; Wade, R. C. Comparative binding energy (COMBINE) analysis of influenza neuraminidase-inhibitor complexes. J. Med. Chem., 2001, 44, 961-971.
48. Perez, C.; Pastor, M.; Ortiz, A. R.; Gago, F. Comparative binding energy analysis of HIV-1 protease inhibitors: incorporation of solvent effects and validation as a powerful tool in receptor-based drug design. J. Med. Chem., 1998, 41, 836-852.
49. Murcia, M.; Morreale, A.; Ortiz, A. R. COMBINE analysis considering multiple receptors: a step towards structure-activity models for protein families J. Med. Chem., 2006, 49, 6241-6253.
50. Lushington, G. H.; Guo, J. X.; Wang, J. L. Whither combine? New opportunities for receptor-based QSAR. Curr. Med. Chem., 2007, 14, 1863-1877.
51. Murcia, M.; Ortiz, A. R. Virtual screening with flexible docking and COMBINE based models. Application to a series of factor Xa inhibitors. J. Med. Chem., 2004, 47, 805-820.
52. Gohlke, H.; Klebe, G. DrugScore meets CoMFA: adaptation of fields for molecular comparison (AFMoC) or how to tailor knowledgebased pair-potentials to a particular protein. J. Med. Chem., 2002, 45, 4153-4170.
53. Breu, B.; Silber, K.; Gohlke, H. Consensus adaptation of fields for molecular comparison (AFMoC) models incorporate ligand and receptor conformational variability into Tailor-made scoring functions. J. Chem. Inf. Model., 2007, 47, 2383-2400.
54. Matter, H.; Will, D. W.; Nazare, M.; Schreuder, H.; Laux, V.; Wehner, V. Structural requirements for factor Xa inhibition by 3-oxybenzamides with neutral P1 substituents: combining X-ray crystallography, 3DQSAR, and tailored scoring functions. J. Med. Chem., 2005, 48, 3290-312.
55. Silber, K.; Heidler, P.; Kurz, T.; Klebe, G. AFMoC enhances predictivity of 3D QSAR: a case study with DOXP-reductoisomerase. J. Med. Chem., 2005, 48, 3547-3563.
56. Radestock, S.; Bohm, M.; Gohlke, H. Improving binding mode predictions by docking into protein-specifically adapted potential fields. J. Med. Chem., 2005, 48, 5466-5479.
57. Oloff, S.; Zhang, S.; Sukimar, N.; Breneman, C.; Tropsha, A. Chemometric analysis of ligand receptor complementarity. Identifying complementary ligands based on receptor information (CoLiBRI). J. Chem. Inf. Model., 200, 46, 844-851.
58. Breneman, C. M.; Thompson, T. R.; Rhem, M.; Dung, M. Electron density modeling of large systems using the transferable atom equivalent method. Comput. Chem., 1995, 19, 161-169.
59. Mazza, C. B.; Sukumar, N.; Breneman, C. M.; Cramer, S. M. Prediction of protein retention in ion-exchange systems using molecular descriptors obtained from crystal structure. Anal. Chem., 2001, 73, 5457-5461.
60. Song, M.; Breneman, C. M.; Bi, J.; Sukumar, N.; Bennett, K. P.; Cramer, S.; Tugcu, N. Prediction of protein retention times in anionexchange chromatography systems using support vector regression. J. Chem. Inf. Comput. Sci., 2002, 42, 1347-1357.
61. http://www.chemistry.mcmaster.ca/faculty/bader/aim/
62. Wang, R.; Fang, X.; Lu, Y.; Yang, C.-Y.; Wang, S. The PDBbind database: methodologies and updates. J. Med. Chem., 2005, 48, 4111-4119.
63. Wang, R.; Fang, X.; Lu, Y.; Wang, S. The PDBbind database: collection of binding affinities for protein-ligand complexes with known three-dimensional structures. J. Med. Chem., 2004, 47, 2977-2980.
64. Datar, P. A.; Khedkar, S. A.; Malde, A. K.; Coutinho, E. C. Comparative residue interaction analysis (CoRIA): a 3D-QSAR approach to explore the binding contributions of active site residues with ligands. J. Comput. Aided Mol. Des., 2006, 20, 343-360.
65. Staton, D. T.; Jurs, P. C. Development and use of charged partial surface area structural descriptors in computer assisted quantitative structure property relationship studies. Anal. Chem., 1990, 62, 2323-2329.
66. Rogers, D.; Hopfinger, A. J. Applications of genetic function approximation to quantitative structure-activity relationships and quantitative structure-property relationships. J. Chem. Inf. Comput. Sci., 1994, 34, 854-866.
67. Santos-Filho, O. A.; Cherkasov, A. Using molecular docking, 3D-QSAR, and cluster analysis for screening structurally diverse data sets of pharmacological interest. J. Chem. Inf. Model., 2008, 48, 2054-2065.
68. Cherkasov, A. 'Inductive' descriptors. 10 successful years in QSAR. Curr. Comput. Aided Drug Des., 2005, 1, 21-42.