Three-dimensional quantitative structure-activity relationships from molecular similarity matrices and genetic neural networks. 1. Method and validations

Journal of Medicinal Chemistry

Volumn 40, Issue 26, 1997, Pages 4347-4359

  So, Sung Sau ^a   Karplus, Martin ^b  

^a HARVARD UNIVERSITY   (United States)

^b UNIVERSITÉ DE STRASBOURG   (France)

Author keywords

[No Author keywords available]

Indexed keywords

STEROID; TRANSCORTIN;

ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPUTER PROGRAM; DIELECTRIC CONSTANT; DRUG PROTEIN BINDING; ELECTRIC FIELD; MOLECULAR MODEL; QUANTITATIVE STRUCTURE ACTIVITY RELATION; REGRESSION ANALYSIS;

ALGORITHMS; ELECTROSTATICS; MODELS, CHEMICAL; MOLECULAR STRUCTURE; NEURAL NETWORKS (COMPUTER); PROTEIN BINDING; STEROIDS; STRUCTURE-ACTIVITY RELATIONSHIP; TRANSCORTIN;

EID: 0031434910     PISSN: 00222623     EISSN: None     Source Type: Journal    
DOI: 10.1021/jm970487v     Document Type: Article

References (59)

1. Goodford, P. J. A computational procedure for determining energetically favorable binding sites on biologically important molecules. J. Med. Chem. 1985, 28, 849-857.
2. Cramer, R. D., III; Patterson, D. E.; Bunce, J. D. Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J. Am. Chem. Soc. 1988, 110, 5959-5967.
3. Dunn, W. J.; Wold, S.; Edlund, U.; Hellberg, S. Multivariate structure-activity relationships between data from a battery of biological tests and an ensemble of structure descriptors: the PLS method. Quant. Struct.-Act. Relat. 1984, 3, 131-137.
4. Kubinyi, H. 3D QSAR in Drug Design: Theory, Methods and Applications; ESCOM Science Publishers B.V.: Leiden, The Netherlands, 1993.
5. Cruciani, G.; 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. 1994, 37, 2589-2601.
6. Dean, P. M. Molecular similarity. In 3D QSAR in Drug Design: Theory, Methods and Applications; Kubinyi, H., Ed.; ESCOM Science Publishers B. V.: Leiden, The Netherlands, 1993; pp 150-172.
7. Richards, W. G. Molecular similarity and dissimilarity. In Modelling of Biomolecular Structures and Mechanisms; Pullman, A., Jortner, J., Pullman, B., Eds.; Kluwer Academic Publishers: Dordrecht, The Netherlands, 1995; pp 365-369.
8. Kubinyi, H. A general view on similarity and QSAR studies. In Computer-Assisted Lead Finding and Optimization: Current Tools for Medicinal Chemistry; van de Waterbeemd, H., Testa, B., Folkers, G., Eds.; VHCA: Basel, and Wiley-VCH: Weinheim, 1997; pp 7-28.
9. Carbo, R.; Leyda, L.; Arnau, M. An electron density measure of the similarity between two compounds. Int. J. Quantum Chem. 1980, 17, 1185-1189.
10. Hodgkin, E. E.; Richards, W. G. Molecular similarity based on electrostatic potential and electric field. Int. J. Quantum Chem., Quantum Biol. Symp. 1987, 14, 105-110.
11. Good, A. C. The calculation of molecular similarity: alternative formulas, data manipulation and graphical display. J. Mol. Graph. 1992, 10, 144-151.
12. Meyer, A. M.; Richards, W. G. Similarity of molecular shape. J. Comput.-Aided Mol. Des. 1991, 5, 421-439.
13. So, S.-S.; Karplus, M. Three-dimensional quantitative structure-activity relationships from molecular similarity matrices and genetic neural networks. 2. Applications. J. Med. Chem. 1997, 40, 4360-4371.
14. Good, A. C.; So, S.-S.; Richards, W. G. Structure-activity relationships from molecular similarity matrices. J. Med. Chem. 1993, 36, 433-438.
15. Cruciani, G.; Clementi, S. GOLPE: philosophy and applications in 3D QSAR. In Advanced Computer-Assisted Techniques in Drug Discovery; van de Waterbeemd, H., Ed.; VCH Publishers, Inc.: New York, 1994; Vol. 3, pp 61-88.
16. Good, A. C.; Peterson, S. J.; Richards, W. G. QSAR's from similarity matrices. Technique validation and application in the comparison of different similarity evaluation methods. J. Med. Chem. 1993, 36, 2929-2937.
17. Benigni, R.; Cotta Ramusino, M.; Giorgi, F.; Gallo, G. Molecular similarity matrices and quantitative structure-activity relationships: a case study with methodological implications. J. Med. Chem. 1995, 38, 629-635.
18. Horwell, D. C.; Howson, W.; Higginbottom, M.; Naylor, D.; Ratcliffe, G. S.; Williams, S. Quantitative structure-activity relationships (QSARs) of N-terminus fragments of NK1 tachykinin antagonists: a comparison of classical QSARs and three-dimensional QSARs from similarity matrices. J. Med. Chem. 1995, 35, 4454-4462.
19. Montanari, C. A.; Tute, M. S.; Beezer, A. E.; Mitchell, J. C. Determination of receptor-bound drug conformations by QSAR using flexible fitting to derive a molecular similarity index. J. Comput.-Aided Mol. Des. 1996, 10, 67-73.
20. Kubinyi, H. Variable selection in QSAR studies. I. An evolutionary algorithm. Quant. Struct.-Act. Relat. 1994, 13, 285-294.
21. So, S.-S.; Karplus, M. Evolutionary optimization in quantitative structure-activity relationship: an application of genetic neural network. J. Med. Chem. 1996, 39, 1521-1530.
22. So, S.-S.; Karplus, M. Genetic neural networks for quantitative structure-activity relationships: improvements and application of benzodiazepine affinity for benzodiazepine/GABAA receptors. J. Med. Chem. 1996, 39, 5246-5256.
23. Jain, A. N.; Koile, K.; Chapman, D. Compass: predicting biological activities from molecular surface properties. Performance comparisons on a steroid benchmark. J. Med. Chem. 1994, 37, 2315-2327.
24. Klebe, G.; Abraham, U.; Mietzner, T. Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. J. Med. Chem. 1994, 37, 4130-4146.
25. Hahn, M.; Rogers, D. Receptor surface models. 2. Application to quantitative structure-activity relationships studies. J. Med Chem. 1995, 38, 2091-2102.
26. 2-guided region selection for comparative molecular field analysis: a simple method to achieve consistent results. J. Med. Chem. 1995, 38, 1060-1066.
27. Wagener, M.; Sadowski, J.; Gasteiger, J. Autocorrelation of molecular surface properties for modeling corticosteroid binding globulin and cytosolic Ah receptor activity by neural networks J. Am. Chem. Soc, 1995, 117, 7769-7775.
28. Kellogg, G. E.; Kier, L. B.; Gaillard, P.; Hall, L. H. Estate fields: applications to 3D QSAR. J. Comput.-Aided Mol. Des. 1996, 10, 513-520.
29. Silverman, B. D.; Platt, D. E. Comparative molecular moment analysis (CoMMA): 3D-QSAR without molecular superposition. J. Med. Chem. 1996, 39, 2129-2140.
30. Oprea, T. I.; Garcia, A. E. Three-dimensional quantitative structure-activity relationships of steroid aromatase inhibitors. J. Comput.-Aided Mol. Des. 1996, 70, 186-200.
31. Gasteiger, J.; Sadowski, J.; Schuur, J.; Selzer, P.; Steinhauer, L.; Steinhauer, V. Chemical information in 3D space. J. Chem. Inf. Comput. Sci. 1996, 36, 1030-1037.
32. Stewart, J. J. P. MOPAC: A semiempirical molecular orbital program. J. Comput.-Aided Mol. Des. 1990, 4, 1-105.
33. Cerius2, version 2.0; Molecular Simulations Inc., San Diego, CA.
34. a's) of clonidine-like imidazolines, 2-substituted imidazoles, and 1-methyl-2-substituted-imidazoles from 3D structures using a comparative molecular field analysis (CoMFA) approach. J. Med. Chem. 1991, 34, 2056-2060.
35. The van der Waals radii are taken from WebElements: http:// www.shef.ac.uk/chemistry/web-elements/web-elements-home.html.
36. Rappe, A. K.; Casewit, C. J.; Colwell, K. S.; Goddard, W. A., III; Skiff, W. M. UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations. J. Am. Chem. Soc. 1992, 114, 5832-5842.
37. Halgren, T. A. Merck molecular force field. I. Basis, form, scope, parametrization, and performance of MMFF94. J. Comput. Chem. 1996, 17, 490-519.
38. Halgren, T. A. Merck molecular force field. II. van der Waals and electrostatic parameters for intermolecular interactions. J. Comput. Chem. 1996, 17, 520-552.
39. Halgren, T. A. Merck molecular force field. III. Molecular geometries and vibrational frequencies for MMFF94. J. Comput. Chem. 1996, 17, 553-586.
40. Halgren, T. A.; Nachbar, R. B. Merck molecular force field. IV. Conform a tional energies and geometries for MMFF94. J. Comput. Chem. 1996, 17, 587-615.
41. Halgren, T. A. Merck molecular force field. V. Extension of MMFF94 using experimental data, additional computational data, and empirical rules. J. Comput. Chem. 1996, 17, 616-641.
42. Holland, J. H. Adaption in Natural and Artificial Systems; The University of Michigan Press: Ann Arbor, MI, 1975.
43. Hertz, J.; Krogh, A.; Palmer, R. G. Introduction to the Theory of Neural Computation; Addison-Wesley Publishing Co.: Redwood City, CA, 1991.
44. Møller, M. F. A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks 1993, 6, 525-533.
45. Andrea, T. A.; Kalayeh, H. Applications of neural networks in quantitative structure-activity relationships of dihydrofolate reductase inhibitors. J. Med. Chem. 1991, 34, 2824-2836.
46. So, S.-S.; Richards, W. G. Application of neural networks: quantitative structure-activity relationships of the derivatives of 2,4-diamino-5-(substituted-benzyl)pyrimidines as DHFR inhibitors. J. Med. Chem. 1992, 35, 3201-3207.
47. Manallack, D. T.; Ellis, D. D.; Livingstone, D. J. Analysis of linear and nonlinear QSAR data using neural networks. J. Med. Chem. 1994, 37, 3758-3767.
48. Luke, B. T. Evolutionary programming applied to the development of quantitative structure-activity relationships and quantitative structure-property relationships. J. Chem. Inf. Comput. Sci. 1994, 34, 1279-1287.
49. An arguably better way to perform cross-validation, as suggested by a reviewer, is to run a series of GNN calculations from the beginning with each compound removed in turn. It is unlikely that this computationally demanding procedure will have any effect on the results.
50. Topliss, J. G.; Edwards, R. P. Chance factors in studies of quantitative structure-activity relationships. J. Med. Chem. 1979, 22, 1238-1244.
51. Wold, S.; Eriksson, L. Statistical validation of QSAR results. In Chemometric Methods in Molecular Design; van de Waterbeemd, H., Ed.; VCH Publishers, Inc.: New York, 1995; Vol. 2, pp 309-318.
52. Gasteiger, J.; Marsili, M. Iterative partial equalization of orbital electronegativity - rapid access to atomic charges. Tetrahedron 1980, 36, 3219-3288.
53. Rappé, A. K.; Goddard, W. A., III. Charge equilibration for molecular dynamics simulations. J. Phys. Chem. 1991, 95, 3358-3363.
54. Maggiora, G. M.; Elrod, D. W. Computational neural networks as model-free mapping devices. J. Chem. Inf. Comput. Sci. 1992, 32, 732-741.
55. Hopfinger, A. J. Theory and application of molecular potential energy fields in molecular shape analysis: a quantitative structure-activity relationship study of 2,4-diamino-5-benzylpyrimidines as dihydrofolate reductase inhibitors. J. Med. Chem. 1983, 26, 990-996.
56. Dunn, J. F.; Nisula, B. C.; Rodbard, D. Transport of steroid hormones: binding of 21 endogenous steroids to both testosterone-binding globulin and corticosteroid-binding globulin in human plasma. J. Clin. Endocrinol. Metab. 1981, 53, 58-68.
57. Pugeat, M. M.; Dunn, J. F.; Nisula, B. C. Transport of steroid hormones: interaction of 70 drugs with testosterone-binding globulin and corticosteroid-binding globulin in human plasma. J. Clin. Endocrinol. Metab. 1981, 53, 69-75.
58. Mickelson, K. E.; Forsthoefel, J.; Westphal, U. Steroid-protein interactions. Human corticosteroid binding globulin: some physicochemical properties and binding specificity. Biochemistry 1981,20, 6211-6218.
59. Westphal, U. Corticosteroid-binding globulin: a review of some recent aspects. Mol. Cell. Biochem. 1983, 55, 145-157.