QSAR Modeling of GPCR Ligands: Methodologies and Examples of Applications

  • A. Tropsha
  • S. X. Wang
Conference paper
Part of the Ernst Schering Foundation Symposium Proceedings book series (SCHERING FOUND, volume 2006/2)


GPCR ligands represent not only one of the major classes of current drugs but the major continuing source of novel potent pharmaceutical agents. Because 3D structures of GPCRs as determined by experimental techniques are still unavailable, ligand-based drug discovery methods remain the major computational molecular modeling approaches to the analysis of growing data sets of tested GPCR ligands. This paper presents an overview of modern Quantitative Structure Activity Relationship (QSAR) modeling. We discuss the critical issue of model validation and the strategy for applying the successfully validated QSAR models to virtual screening of available chemical databases. We present several examples of applications of validated QSAR modeling approaches to GPCR ligands. We conclude with the comments on exciting developments in the QSAR modeling of GPCR ligands that focus on the study of emerging data sets of compounds with dual or even multiple activities against two or more of GPCRs.


Partial Little Square Quantitative Structure Activity Relationship Virtual Screening Target Property Quantitative Structure Activity Relationship Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported in part by the NIH research grant GM066940 and by Berlex Biosciences. We appreciate fruitful discussions with Drs. R. Horuk and Sabine Schlyer.


  1. Becker OM, Dhanoa DS, Marantz Y, Chen D, Shacham S, Cheruku S, Heifetz A, Mohanty P, Fichman M, Sharadendu A, Nudelman R, Kauffman M, Noiman S (2006) An integrated in silico 3D model-driven discovery of a novel, potent, and selective amidosulfonamide 5-HT1A agonist (PRX-00023) for the treatment of anxiety and depression. J Med Chem 49:3116–3135PubMedCrossRefGoogle Scholar
  2. Bissantz C, Bernard P, Hibert M, Rognan D (2003) Protein-based virtual screening of chemical databases. II. Are homology models of G-protein coupled receptors suitable targets? Proteins 50:5–25PubMedCrossRefGoogle Scholar
  3. Blower PE, Yang C, Fligner MA, Verducci JS, Yu L, Richman S, Weinstein JN (2002) Pharmacogenomic analysis: correlating molecular substructure classes with microarray gene expression data. Pharmacogenomics J 2:259–271PubMedCrossRefGoogle Scholar
  4. Bordas B, Komives T, Szanto Z, Lopata A (2000) Comparative three-dimensional quantitative structure-activity relationship study of safeners and herbicides. J Agric Food Chem 48:926–931PubMedCrossRefGoogle Scholar
  5. Charifson PS, Wyrick SD, Hoffman AJ, Simmons RM, Bowen JP, McDougald DL, Mailman RB (1988) Synthesis and pharmacological characterization of 1-phenyl-, 4-phenyl-, and 1-benzyl-1,2,3,4-tetrahydroisoquinolines as dopamine receptor ligands. J Med Chem 31:1941–1946PubMedCrossRefGoogle Scholar
  6. Charifson PS, Bowen JP, Wyrick SD, Hoffman AJ, Cory M, McPhail AT, Mailman R B (1989) Conformational analysis and molecular modeling of 1-phenyl-, 4-phenyl-, and 1-benzyl-1,2,3,4-tetrahydroisoquinolines as D1 dopamine receptor ligands. J Med Chem 32:2050–2058PubMedCrossRefGoogle Scholar
  7. Chemical Diversity (2004) ChemDiv Chemical Database. Cited 28 November 2006Google Scholar
  8. Cho SJ, Zheng W, Tropsha A (1998) Rational combinatorial library design. Rational design of targeted combinatorial peptide libraries using chemical similarity probe and the inverse QSAR approaches. J Chem Inf Comput Sci 38:259–268PubMedCrossRefGoogle Scholar
  9. Clark RD, Sprous DG, Leonard JM (2001) Validating models based on large dataset. In: Höltje H-D, Sippl W (eds) Rational approaches to drug design, Proceedings of the 13th European Symposium on Quantitative Structure-Activity Relationship, Aug 27–Sept 1. Prous Science, Düsseldorf, pp 475–485Google Scholar
  10. Cramer RD III, Patterson DE, Bunce JD (1988) Comparative Molecular Field Analysis (CoMFA) Effect of Shape on Binding of Steroids to Carrier Proteins. J Am Chem Soc 110:5959–5967PubMedCrossRefGoogle Scholar
  11. Cramer RD III, Patterson DE, Bunce JD (1989) Recent advances in comparative molecular field analysis (CoMFA). Prog Clin Biol Res 291:161–165PubMedGoogle Scholar
  12. Creese I, Iversen SD (1973) Blockage of amphetamine induced motor stimulation and stereotypy in the adult rat following neonatal treatment with 6-hydroxydopamine. Brain Res 55:369–382PubMedCrossRefGoogle Scholar
  13. De Cerqueira LP, Golbraikh A, Oloff S, Xiao Y, Tropsha A (2006) Combinatorial QSAR modeling of P-glycoprotein substrates. J Chem Inf Model 46:1245–1254CrossRefGoogle Scholar
  14. Downs GM, Willett P (1996) Similarity searching in databases of chemical structures. In: Lipkowitz KB, Boyd D (eds) Reviews in computational chemistry. VCH Publishers, New York, pp 1–65Google Scholar
  15. EduSoft L (2003) MolconnZ version 4.05. [4.05]Google Scholar
  16. Fan Y, Shi LM, Kohn KW, Pommier Y, Weinstein JN (2001) Quantitative structure-antitumor activity relationships of camptothecin analogues: cluster analysis and genetic algorithm-based studies. J Med Chem 44:3254–3263PubMedCrossRefGoogle Scholar
  17. Flower DR (1999) Modelling G-protein-coupled receptors for drug design. Biochim Biophys Acta 1422:207–234PubMedCrossRefGoogle Scholar
  18. Girones X, Gallegos A, Carbo-Dorca R (2000) Modeling antimalarial activity: application of kinetic energy density quantum similarity measures as descriptors in QSAR. J Chem Inf Comput Sci 40:1400–1407PubMedCrossRefGoogle Scholar
  19. Golbraikh A, Tropsha A (2002a) Beware of q 2! J Mol Graph Model 20:269–276PubMedCrossRefGoogle Scholar
  20. Golbraikh A, Tropsha A (2002b) Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection. J Comput Aided Mol Des 16:357–369PubMedCrossRefGoogle Scholar
  21. Golbraikh A, Shen M, Xiao Z, Xiao YD, Lee KH, Tropsha A (2003) Rational selection of training and test sets for the development of validated QSAR models. J Comput Aided Mol Des 17:241–253PubMedCrossRefGoogle Scholar
  22. Gussio R, Pattabiraman N, Kellogg GE, Zaharevitz DW (1998) Use of 3D QSAR methodology for data mining the National Cancer Institute Repository of Small Molecules: application to HIV-1 reverse transcriptase inhibition. Methods 14:255–263PubMedCrossRefGoogle Scholar
  23. Hibert MF, Trumpp-Kallmeyer S, Bruinvels A, Hoflack J (1991) Three-dimensional models of neurotransmitter G-binding protein-coupled receptors. Mol Pharmacol 40:8–15PubMedGoogle Scholar
  24. Horn F, Weare J, Beukers MW, Horsch S, Bairoch A, Chen W, Edvardsen O, Campagne F, Vriend G (1998) GPCRDB: an information system for G protein-coupled receptors. Nucleic Acids Res 26:275–279PubMedCentralPubMedCrossRefGoogle Scholar
  25. Kebabian JW, Calne DB (1979) Multiple receptors for dopamine. Nature 277:93–96PubMedCrossRefGoogle Scholar
  26. Kovatcheva A, Golbraikh A, Oloff S, Xiao YD, Zheng W, Wolschann P, Buchbauer G, Tropsha A (2004) Combinatorial QSAR of ambergris fragrance compounds. J Chem Inf Comput Sci 44:582–595PubMedCrossRefGoogle Scholar
  27. Kozikowski AP, Roth B, Tropsha A (2006) Why academic drug discovery makes sense. Science 313:1235–1236PubMedCrossRefGoogle Scholar
  28. Kubinyi H, Hamprecht FA, Mietzner T (1998) Three-dimensional quantitative similarity-activity relationships (3D QSiAR) from SEAL similarity matrices. J Med Chem 41:2553–2564PubMedCrossRefGoogle Scholar
  29. Maybridge (2005) htmlGoogle Scholar
  30. Minor DL, Wyrick SD, Charifson PS, Watts VJ, Nichols DE, Mailman RB (1994) Synthesis and molecular modeling of 1-phenyl-1,2,3,4-tetrahydroisoquinolines and related 5,6,8,9-tetrahydro-13bH-dibenzo[a,h]quinolizines as D1 dopamine antagonists. J Med Chem 37:4317–4328PubMedCrossRefGoogle Scholar
  31. Moron JA, Campillo M, Perez V, Unzeta M, Pardo L (2000) Molecular determinants of MAO selectivity in a series of indolylmethylamine derivatives: biological activities, 3D-QSAR/CoMFA analysis, and computational simulation of ligand recognition. J Med Chem 43:1684–1691PubMedCrossRefGoogle Scholar
  32. National Cancer Institute (2004) Smiles strings. Cited 28 November 2006Google Scholar
  33. National Cancer Institute (2005) htmlGoogle Scholar
  34. Norinder U (1996) Single and domain made variable selection in 3D QSAR applications. J Chemomet 10:95–105CrossRefGoogle Scholar
  35. Novellino E, Fattorusso C, Greco G (1995) Use of comparative molecular field analysis and cluster analysis in series design. Pharm Acta Helv 70:149–154CrossRefGoogle Scholar
  36. Okuno Y, Yang J, Taneishi K, Yabuuchi H, Tsujimoto G (2006) GLIDA: GPCR-ligand database for chemical genomic drug discovery. Nucleic Acids Res 34:D673–D677PubMedCentralPubMedCrossRefGoogle Scholar
  37. Oloff S, Mailman RB, Tropsha A (2005) Application of validated QSAR models of D1 dopaminergic antagonists for database mining. J Med Chem 48:7322–7332PubMedCrossRefGoogle Scholar
  38. Oprea TI (2001) Rapid estimation of hydrophobicity for virtual combinatorial library analysis. SAR QSAR Environ Res 12:129–141PubMedCrossRefGoogle Scholar
  39. Oprea TI, Garcia A E (1996) Three-dimensional quantitative structure-activity relationships of steroid aromatase inhibitors. J Comput Aided Mol Des 110:186–200CrossRefGoogle Scholar
  40. Phillips AG, Fibiger HC (1973) Dopaminergic and noradrenergic substrates of positive reinforcement: differential effects of d- and l-amphetamine. Science 179:575–577PubMedCrossRefGoogle Scholar
  41. Pijnenburg AJ, Honig WM, Van der Heyden JA, Van Rossum JM (1976) Effects of chemical stimulation of the mesolimbic dopamine system upon locomotor activity. Eur J Pharmacol 35:45–58PubMedCrossRefGoogle Scholar
  42. Recanatini M, Cavalli A, Belluti F, Piazzi L, Rampa A, Bisi A, Gobbi S, Valenti P, Andrisano V, Bartolini M, Cavrini V (2000) SAR of 9-amino-1,2,3,4-tetrahydroacridine-based acetylcholinesterase inhibitors: synthesis, enzyme inhibitory activity, QSAR, and structure-based CoMFA of tacrine analogues. J Med Chem 43:2007–2018PubMedCrossRefGoogle Scholar
  43. Roth BL, Kroeze WK (2006) Screening the receptorome yields validated molecular targets for drug discovery. Curr Pharm Des 12:1785–1795PubMedCrossRefGoogle Scholar
  44. Schulz DW, Wyrick SD, Mailman RB (1984) [3H]SCH23390 has the characteristics of a dopamine receptor ligand in the rat central nervous system. Eur J Pharmacol 106:211–212PubMedCrossRefGoogle Scholar
  45. Seeman P, Bzowej NH, Guan HC, Bergeron C, Reynolds GP, Bird ED, Riederer P, Jellinger K, Tourtellotte WW (1987) Human brain D 1 and D 2 dopamine receptors in schizophrenia, Alzheimer's, Parkinson's, and Huntington's diseases. Neuropsychopharmacology 1:5–15PubMedCrossRefGoogle Scholar
  46. Shay JW, Wright WE (2006) Telomerase therapeutics for cancer: challenges and new directions. Nat Rev Drug Discov 5:577–584PubMedCrossRefGoogle Scholar
  47. Shen M, LeTiran A, Xiao Y, Golbraikh A, Kohn H, Tropsha A (2002) Quantitative structure-activity relationship analysis of functionalized amino acid anticonvulsant agents using k nearest neighbor and simulated annealing PLS methods. J Med Chem 45:2811–2823PubMedCrossRefGoogle Scholar
  48. Shen M, Beguin C, Golbraikh A, Stables J, Kohn H, Tropsha A (2004) Application of predictive QSAR models to database mining: identification and experimental validation of novel anticonvulsant compounds. J Med Chem 47:2356–2364PubMedCrossRefGoogle Scholar
  49. Strange PG (1993) Brain biochemistry and brain disorders. Oxford University Press, New YorkGoogle Scholar
  50. Sutherland JJ, Weaver DF (2004) Three-dimensional quantitative structure-activity and structure-selectivity relationships of dihydrofolate reductase inhibitors. J Comput Aided Mol Des 18:309–331PubMedCrossRefGoogle Scholar
  51. Suzuki T, Ide K, Ishida M, Shapiro S (2001) Classification of environmental estrogens by physicochemical properties using principal component analysis and hierarchical cluster analysis. J Chem Inf Comput Sci 41:718–726PubMedCrossRefGoogle Scholar
  52. Topliss JG, Edwards RP (1979) Chance factors in studies of quantitative structure-activity relationships. J Med Chem 22:1238–1244PubMedCrossRefGoogle Scholar
  53. Tropsha A, Zheng W (2001) Identification of the descriptor pharmacophores using variable selection QSAR: applications to database mining. Curr Pharm Des 7:599–612PubMedCrossRefGoogle Scholar
  54. Wold S, Eriksson L (1995) Statistical validation of QSAR results. In: Waterbeemd HVD (ed) Chemometrics methods in molecular design. VCH pp 309–318Google Scholar
  55. Zefirov NS, Palyulin VA (2001) QSAR for boiling points of “small” sulfides. Are the “high-quality structure-property-activity regressions” the real high quality QSAR models? J Chem Inf Comput Sci 41:1022–1027PubMedCrossRefGoogle Scholar
  56. Zhang Y, Devries ME, Skolnick J (2006) Structure modeling of all identified G protein-coupled receptors in the human genome. PLoS Comput Biol 2:e13PubMedCentralPubMedCrossRefGoogle Scholar
  57. Zheng W, Tropsha A (2000) Novel variable selection quantitative structure–property relationship approach based on the k-nearest-neighbor principle. J Chem Inf Comput Sci 40:185–194PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  1. 1.The Laboratory for Molecular Modeling, CB#7360Beard Hall, School of PharmacyNorth CarolinaUSA

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