Skip to main content
SpringerLink
Log in

Recent progress in CoMFA methodology and related techniques

  • Published:
Perspectives in Drug Discovery and Design

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

References

  1. Doweyko, A. M., The hypothetical active site lattice: An approach to modeling sites from data on inhibitor molecules, J. Med. Chem., 31 (1988) 1396-1406.

    Google Scholar 

  2. Ghose, A., Crippen, G., Revankar, G., McKernan, P., Smee, D. and Robbins, R., Analysis of the in vitro activity of certain ribonucleosides against parainfluenza virus using a novel computer-aided molecular modeling procedure, J. Med. Chem., 32 (1989) 746-756.

    Google Scholar 

  3. Cramer, R. D., Patterson, D. E. and Bunce, J. C., Comparative molecular field analysis (CoMFA):1. Effect of shape on binding of steroids to carrier proteins, J. Am. Chem. Soc., 110 (1988) 5959-5967.

    Google Scholar 

  4. Norinder, U., A PLS QSAR analysis using 3D generated aromatic descriptors of principal property type: Application to some dopamine D2 benzamide antagonists, J. Comput.-Aided Mol. Design, 7 (1993) 671-682.

    Google Scholar 

  5. Floersheim, P., Nozulak, J. and Weber, J., Experience with molecular field analysis, In Wermuth, C. G. (Ed.) Trends in QSAR and molecular modeling 92: Proceedings of the 9th European Symposium on Structure-Activity Relationships - QSAR and Molecular Modeling, ESCOM, Leiden, The Netherlands, 1993, pp. 227-232.

  6. Kubinyi, H. (Ed.), 3D QSAR in drug design: Theory, methods and applications, ESCOM, Leiden, The Netherlands, 1993.

    Google Scholar 

  7. Jain, A. N., Harris, N. L. and Park, J. Y., Quantitative binding site model generation: Compass applied to multiple chemotypes targeting the 5-HT 1A receptor, J. Med. Chem., 38 (1995) 1295-1308.

    Google Scholar 

  8. Head., R. D., Smythe, M. L., Oprea, T. I., Waller, C. L., Green, S. M. and Marshall, G. R., VALIDATE: A new method for the receptor-based prediction of binding affinities of novel ligands, J. Am. Chem. Soc., 118 (1996) 3959-3969.

    Google Scholar 

  9. Anzali, S., Barnickel, G., Krug, M., Sadowski, J., Wagener, M., Gastaiger, J. and Polanski, J., The comparison of geometric and electronic properties of molecular surfaces by neural networks: Application to the analysis of corticosteroid-binding globulin activity of steroids, J. Comput.-Aided Mol. Des., 10 (1996) 521-534.

    Google Scholar 

  10. Rogers, D. R. and Hopfinger, A. J., Application of genetic function approximation to quantitative structure-activity relationships and quantitative structure-property relationships, J. Chem. Inf. Comput. Sci., 34 (1994) 854-866.

    Google Scholar 

  11. Silverman, B. D. and Platt, D. E.,Comparative molecular moment analysis (CoMMA): 3D-QSAR without molecular superposition, J. Med. Chem., 39 (1996) 2129-2140.

    PubMed  Google Scholar 

  12. Ortiz, A. R., Pisabarro, M. T., Gago, F. and Wade, R., Prediction of drug binding affinities by comparative binding energy analysis, J. Med. Chem., 38 (1995) 2681-2691.

    Google Scholar 

  13. Gusso, R., Pattabiraman, N., Zaharevitz, D. W., Kellogg, G. E., Topol, I. A., Rice, W. G., Schaeffer, C. A., Erickson, J. W. and Burt, S. K., All-atom models for the non-nucleoside binding site of HIV-1 reverse transcriptase complexed with inhibitors: A 3D QSAR approach, J. Med. Chem., 39 (1996) 1645-1650.

    Google Scholar 

  14. Klebe, G. and Abraham, U., On the prediction of binding properties of drug molecules by comparative molecular field analysis, J. Med. Chem., 36 (1993) 70-80.

    PubMed  Google Scholar 

  15. DePriest, S. A., Mayer, D., Naylor, C. B. and Marshall, G. R., 3D-QSAR of angiotensin-converting enzyme and thermolysin inhibitors: A comparison of CoMFA models based on deduced and experimentally determined active site geometries, J. Am. Chem. Soc., 115 (1993) 5372-5384.

    Google Scholar 

  16. Folkers, G., Merz, A. and Rognan, D., CoMFA: Scope and limitations, In Kubinyi, H. (Ed.) 3D QSAR in drug design: Theory, methods and applications, ESCOM, Leiden, The Netherlands, 1993, pp. 583-618.

    Google Scholar 

  17. Waller, C. L., Oprea, T. I., Giolitti, A. and Marshall, G. R., Three-dimensional QSAR of human immunodeficiency virus (I) protease inhibitors: 1. A CoMFA study employing experimentally-determined alignment rules, J. Med. Chem., 36 (1993) 4152-4160.

    PubMed  Google Scholar 

  18. Waller, C. L. and Marshall, G. R., Three-dimensional quantitative structure-activity relationship of angiotensin-converting enzyme and thermolysin inhibitors: 2. A comparison of CoMFA models incorporating molecular orbital fields and desolvation free energies based on active-analog and complementary-receptor-field alignment rules, J. Med. Chem., 36 (1993) 2390-2403.

    Google Scholar 

  19. Oprea, T. I., Waller, C. L. and Marshall, G. R., Three-dimensional quantitative structure-activity relationship of human immunodeficiency virus (I) protease inhibitors: 2. Predictive power using limited exploration of alternative binding modes, J. Med. Chem., 37 (1994) 2206-2215.

    Google Scholar 

  20. Brandt, W., Lehmann, T., Willkomm, C., Fittkau, S. and Barth, A., CoMFA investigation on two series of artificial peptide inhibitors of the serine protease thermitase, Int. J. Peptide Protein Res., 46 (1995) 73-78.

    Google Scholar 

  21. Kroemer, R. T., Ettmayer, P. and Hecht, P., 3D-quantitative structure-activity relationships of human immunodeficiency virus type-1 protease inhibitors: Comparative molecular field analysis of 2-heterosubstituted statine derivatives - implications for the design of novel inhibitors, J. Med. Chem., 38 (1995) 4917-4928.

    Google Scholar 

  22. Cruciani, G. and Watson, K. A., Comparative molecular field analysis using GRID force-field and GOLPE variable selection methods in a study of inhibitors of glycogen phosphorylase b, J. Med. Chem., 37 (1994) 2589-2601.

    Google Scholar 

  23. Gamper, A. M., Winger, R. H., Liedl, K. R., Sotriffer, C. A., Varga, J. M., Kroemer, R. T. and Rode, B. M., Comparative molecular field analysis of haptens docked to the multispecific antibody IgE (Lb4), J. Med. Chem., 39 (1996) 3882-3888.

    Google Scholar 

  24. Goodsell, D. S. and Olson, A. J., Automated docking of substrates to proteins by simulated annealing, Proteins: Struct. Funct. Genet., 8 (1990) 195-202.

    Google Scholar 

  25. Cho, J.-C., Garsia, M. L. S., Bier, J. and Tropsha, A., Structure-based alignments and comparative molecular field analysis of acetylcholinesterase inhibitors, J. Med. Chem., 39 (1996) 5064-5071.

    Google Scholar 

  26. Kroemer, R. T. and Hecht, P., A new procedure for improving the predictiveness of CoMFA models and its application to a set of dihydrofolate reductase inhibitors, J. Comput.-Aided Mol. Design, 9 (1995) 396-406.

    Google Scholar 

  27. Kroemer, R. T., Hecht, P., Guessregen, S. and Liedl, K. R., Improving the predictive quality of CoMFA models, In Kubinyi, H., Folkers, G. and Martin, Y. C. (Eds.) 3D QSAR in drug design: Vol. 3, Kluwer Academic Publishers, Dordrecht, The Netherlands, 1998, pp. 41-56.

    Google Scholar 

  28. Norinder, U., 3D-QSAR investigation of the tripos benchmark steroids and some protein-tyrosine kinase inhibitors of styrene type using the TDQ approach, J. Chemometrics, 10 (1996) 533-545.

    Google Scholar 

  29. Jain, A. N., Koile, K. and Chapman, D., Compass: Predicting biological activities from molecular surface properties. Performance comparisons on a steroid benchmark, J. Med. Chem., 37 (1994) 2315-2327.

    PubMed  Google Scholar 

  30. Catalyst, Molecular Simulations Inc., San Diego, CA, U. S. A.

  31. Norinder, U., The alignment problem in 3D-QSAR: A combined approach using catalyst and a 3D-QSAR technique, In Sanz, F., Giraldo, J. and Manaut, F. (Eds.) QSAR and molecular modeling: Concepts, computational tools and biological applications, Prous Science Publishers, Barcelona, Spain, 1995, pp. 433-438.

    Google Scholar 

  32. Palomer, A., Giolitti, A., Garcia, M. L., Cabre, F., Mauleon, D. and Carganico, G., Molecular modeling and CoMFA investigations on LTD4 receptor antagonists, In Sanz, F., Giraldo, J. and Manaut, F. (Eds.) QSAR and molecular modeling: Concepts, computational tools and biological applications, Prous Science Publishers, Barcelona, Spain, 1995, pp. 444-450.

    Google Scholar 

  33. Hoffmann, R. D. and Langer, T., Use of the Catalyst program as a new alignment tool for 3D-QSAR, In Sanz, F., Giraldo, J. and Manaut, F. (Eds.) QSAR and molecular modeling: concepts, computational tools and biological applications, Prous Science Publishers, Barcelona, Spain, 1995, pp. 466-469.

    Google Scholar 

  34. For a review of methods of alignments of molecules see Klebe, G., Structural alignment of molecules, In Kubinyi, H. (Ed.) 3D QSAR in drug design: Theory, methods and applications, ESCOM, Leiden, The Netherlands, 1993, pp. 173-199.

    Google Scholar 

  35. Klebe, G., Abraham, U. and Mietzner, T., Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity, J. Med. Chem. 37 (1994) 4130-4146.

    Google Scholar 

  36. Kellogg, G. E., Semus, S. F. and Abraham, D. J., HINT: A new method of empirical field calculation of CoMFA, J. Comput.-Aided Mol. Design, 5 (1991) 545-552.

    Google Scholar 

  37. Kellogg, G. E. and Abraham, D. J., Hydrophobic fields, In Kubinyi, H. (Ed.) 3D QSAR in drug design: Theory, methods and applications, ESCOM, Leiden, The Netherlands, 1993, pp. 506-522.

    Google Scholar 

  38. Goodford, P. J., A Computational procedure for determining energetically favorable binding sites on biologically important macromolecules, J. Med. Chem., 28 (1985) 849-857.

    PubMed  Google Scholar 

  39. Wade, R. C., Molecular interaction fields, In Kubinyi, H. (Ed.) 3D QSAR in drug design: Theory, methods and applications, ESCOM, Leiden, The Netherlands, 1993, pp. 486-505.

    Google Scholar 

  40. Kim, K. H., Greco, G., Novellino, E., Silipo, C. and Vittoria, A., Use of the hydrogen bond potential function in a comparative molecular field analysis (CoMFA) on a set of benzodiazepines, J. Comput.-Aided Mol. Design, 7 (1993) 263-280.

    Google Scholar 

  41. Davis, A. M., Gensmantel N. P., Johansson, E. and Marriott, D. P., The use of the GRID program in the 3D QSAR analysis of a series of calcium-channel agonists, J. Med. Chem., 37 (1994) 963-972.

    Google Scholar 

  42. Kim, K. H., A novel method of describing hydrophobic effects directly from 3D structures in 3Dquantitative structure-activity relationships study, Med. Chem. Res., 1 (1991) 259-264.

    Google Scholar 

  43. Kim, K. H., 3D-Quantitative structure-activity relationships: Describing hydrophobic interactions directly from 3D structures using a comparative molecular field analysis (CoMFA) approach, Quant. Struct.-Act. Relat., 12 (1993) 232-238.

    Google Scholar 

  44. Kenny, P. W., Prediction of hydrogen bond basicity from computed molecular electrostatic properties: Implications for comparative molecular field analysis, J. Chem. Soc. Perkin Trans., 2 (1994) 199-202.

    Google Scholar 

  45. Fuchère, J. L., Quarendon, P. and Kaetterer, L. J., Estimating and representing hydrophobicity potential, J. Mol. Graph., 8 (1988) 202-206.

    Google Scholar 

  46. For a recent review see Testa, B., Carrupt, P. A., Gaillard, P., Billois, F. and Weber, P., Lipophilicity in molecular modeling, Pharm. Res., 13 (1996) 335-343.

    PubMed  Google Scholar 

  47. Gaillard, P., Carrupt, P. A., Testa, B. and Schambel, P., Binding of arylpiperazines, (aryloxy) propanolamines and tetrahydropyridyl-indoles to the 5-HT 1A receptor: Contribution of the molecular Recent Progress in CoMFA Methodology and Related Techniques lipophilicity potential to three-dimensional quantitative structure-activity relationship models, J. Med. Chem., 39 (1996) 126-134.

    Google Scholar 

  48. Kneubühler, S., Thull, U., Altomare, C., Carta, V., Gaillard, P., Carrupt, P. A., Carotti, A. and Testa, B., Inhibition of monoamine oxidase-B by 5H-indeno[1,2-c]pyridazine derivatives: Biological activities, quantitative structure-activity relationships (QSARs) and 3D-QSARs, J. Med. Chem., 38 (1995) 3874-3883.

    Google Scholar 

  49. Thull, U., Kneubühler, S., Gaillard, P., Carrupt, P. A., Testa, B., Altomare, C., Carotti, A., Jenner, P. and McNaught, K. S. P., Inhibition of monoamine oxidase by isoquinoline derivatives: Qualitative and 3Dquantitative structure-activity relationships, Biochem. Pharmacol., 50 (1995) 869-877.

    Google Scholar 

  50. Masuda, T., Nakamura, K., Jikihara, T., Kasuya, F., Igarashi, K., Fukui, M., Takagi, T. and Fujiwara, H., 3D-quantitative structure-activity relationships for hydrophobic interactions: Comparative molecular field analysis (CoMFA) including molecular lipophilicity potentials as applied to the glycine conjugation of aromatic as well as aliphatic carboxylic acids, Quant. Struct.-Act. Relat., 15 (1996) 194-200.

    Google Scholar 

  51. Norinder, U., Experimental design based 3-D QSAR analysis of steroid-protein interactions: Application to human CBG complexes, J. Comput.-Aided Mol. Design, 4 (1990) 381-389.

    Google Scholar 

  52. Poso, A., Tuppurainen, K. and Gynther, J., Modeling of molecular mutagenicity with comparative molecular field analysis (CoMFA): Structural and electronic properties of MX compounds related to TA100 mutagenicity, J. Mol. Struc. (Theochem), 304 (1994) 255-260.

    Google Scholar 

  53. Navajas, C., Poso, A., Tuppurainen, K. and Gynther, J. Comparative molecular field analysis (CoMFA) of MX compounds using different semi-empirical methods: LUMO field and its correlation with mutagenic activity, Quant. Struct.-Act. Relat., 15 (1996) 189-193.

    Google Scholar 

  54. Kim, K. H. and Martin, Y. C., Direct prediction of linear free energy substituent effects from 3D structures using comparative molecular field analysis: 1. Electronic effects of substituted benzoic acids, J. Org. Chem., 56 (1991) 2723-2729.

    Google Scholar 

  55. Kim, K. H. and Martin, Y. C., Direct prediction of dissociation constants (pKa's) of clonidine-line imidazolines, 2-substituted imidazoles, and 1-methy-2-substituted-imidazoles from 3D structures using a comparative molecular field analysis (CoMFA) approach, J. Med. Chem., 34 (1991) 2056-2060.

    Google Scholar 

  56. Kroemer, R. T., Hecht, P. and Liedl, K. R., Different electrostatic descriptors in Comparative molecular field analysis: A comparison of molecular electrostatic and coulumb potentials, J. Comput. Chem., 17 (1996) 1296-1308.

    Google Scholar 

  57. Gasteiger, J. and Marsili, M., Iterative partial equalization of orbital electronegativity - a rapid access to atomic charges, Tetrahedron, 36 (1980) 3219-3288.

    Google Scholar 

  58. Kellogg, G. E., Kier, L. B., Gaillard, P. and Hall, L. H., E-state fields: Applications to 3D QSAR, J. Comput.-Aided Mol. Design, 10 (1996) 513-520.

    Google Scholar 

  59. Hall, L. H. and Kier, L. H., Binding of salicylamides: QSAR analysis with electrotopological state indices, Med. Chem. Res., 2 (1992) 497-502.

    Google Scholar 

  60. Delphi, Molecular simulations,San Diego, CA, U. S. A.

  61. Gilson, M. K. and Honig, B. H., Calculations of electrostatic potentials in an active site, Nature, 330 (1987) 84-86.

    Google Scholar 

  62. Waller, C. L. and Kellogg, G. E., Adding chemical information to CoMFA models with alternative 3D QSAR fields, NetSci, January 1996: http://www. awod. com/netsci/Science/Compchem/feature10. html.

  63. Kroemer, R. T. and Hecht, P., Replacement of steric 6-12 potential-derived interaction energies by atombased indicator variables in CoMFA leads to models of higher consistency, J. Comput.-Aided Mol. Design, 9 (1995) 205-212.

    Google Scholar 

  64. Wold, S., Johansson, E. and Cocchi, M., PLS - partial least-squares projections to latent structures, In Kubinyi, H. (Ed.) 3D QSAR in drug design: Theory, methods and applications, ESCOM, Leiden, The Netherlands, 1993, pp. 523-550.

    Google Scholar 

  65. Baroni, M., Constantino, G., Cruciani, G., Riganelli, D., Valigi, R. and Clementi, S., Generating optimal linear PLS estimations (GOLPE): An advanced chemometric tool for handling 3D QSAR problems, Quant. Struct.-Act. Relat., 12 (1993) 9-20.

    Google Scholar 

  66. Norinder, U., Single and domain mode variable selection in 3D QSAR applications, J. Chemometrics, 10 (1996) 95-105.

    Google Scholar 

  67. Nordén, B., Svensson, P. and Carter, R. E., oral presentation at the 10th European Symposium on Structure-Activity Relationships, Barcelona, 1994.

  68. Cho, S.-J. and Tropsha, A., Cross-validated R2-guided region selection for comparative molecular field analysis: A simple method to achieve consistent results, J. Med. Chem., 38 (1995) 1060-1066.

    PubMed  Google Scholar 

  69. Cruciani, G., Pastor, M. and Clementi, S., Region selection in 3D QSAR, In van der Waterbeemd, H. (Ed.) Computer lead finding and optimization: Proceedings of the 11th European Symposium on Structure-Activity Relationships, Wiley-VCH, Basel, Switzerland, 1977, pp. 379-395.

    Google Scholar 

  70. Pastor, M., Cruciani, G. and Clementi, S., Smart Region Definition SRD: A new way to improve the predictive ability and interpretability of three-dimensional quantitative structure-activity relationships, J. Med. Chem., 40 (1997) 1455-1464.

    Google Scholar 

  71. Cho, S.-J., Tropsha, A., Suffness, M., Cheng Y.-C. and Lee, K.-H., Antitumor agents: 163. Three-dimensional quantitative structure-activity relationship study of 4′-O-demethylepipodophyllotoxin analogs using the modified CoMFA/q2-GRS approach, J. Med. Chem., 39 (1996) 1383-1395.

    PubMed  Google Scholar 

  72. Sutter, J. M., Dixon, S. L. and Jurs, P. C., Automated descriptor selection for quantitative structure-activity relationships using generalized simulated annealing, J. Chem. Inf. Comput. Sci., 35 (1995) 77-84.

    Google Scholar 

  73. Rännar, S., Lindgren, F., Geladi, P. and Wold, S., A PLS kernel algorithm for data sets with many variables and fewer objects: Part 1. Theory and algorithm, J. Chemometrics, 8 (1994) 111-125.

    Google Scholar 

  74. Rännar, S., Geladi, P., Lindgren, F. and Wold, S., A PLS kernel algorithm for data sets with many variables and fewer objects: Part 2. Cross-validation, missing data and examples, J. Chemometrics, 9 (1995) 459-470.

    Google Scholar 

  75. Bush, B. L. and Nachbar, Jr., R. B., Sample-distance partial least squares: PLS optimized for many variables, with application to CoMFA, J. Comput.-Aided Mol. Design, 7 (1993) 587-619.

    Google Scholar 

  76. Seethe chapter by F. Lindgren and S. Rännar in this volume, pp. 105-113, for a more detailed presentation of kernel PLS methods.

  77. Bro, R., Multiway calibration: Multilinear PLS, J. Chemometrics, 10 (1996) 47-61.

    Google Scholar 

  78. Nilsson, J., Bro, R., Wikström, H. and Smilde, A., A comparison between multi-way PLS and GOLPE utilised as variable selection tools, applied on GRID-parameters from a set of compounds with affinity for the dopamine D 3 receptor subtype. Poster presentation at the 11th European symposium on Structure-Activity Relationships, Lausanne, 1996.

  79. Nilsson, J. and Smilde, A., Multiway calibration in 3D QSAR, J. Chemometrics (in press).

  80. Nilsson, J., personal communication.

  81. Dunn III, W. J., Hopfinger, A. J., Catana, C. and Duraiswami, C., Solution of the conformation and alignment tensors for the binding of trimethoprim and its analogs to dihydrofolate reductase: 3D-quantitative structure-activity relationship study using molecular shape analysis, 3-way partial least squares regression, and 3-way factor analysis, J. Med. Chem. 39 (1996) 4825-4832.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Astra Pain Control AB, S-151 85, Södertälje, Sweden

    Ulf Norinder

Authors
  1. Ulf Norinder
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Norinder, U. Recent progress in CoMFA methodology and related techniques. Perspectives in Drug Discovery and Design 12, 25–39 (1998). https://doi.org/10.1023/A:1017057332537

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1017057332537

Keywords

Search

Navigation