Genetic neural networks for quantitative structure-activity relationships: Improvements and application of benzodiazepine affinity for benzodiazepine/GABAA receptors

Journal of Medicinal Chemistry

Volumn 39, Issue 26, 1996, Pages 5246-5256

  So, Sung Sau ^a   Karplus, Martin ^a,b  

^a HARVARD UNIVERSITY   (United States)

^b UNIVERSITÉ DE STRASBOURG   (France)

Author keywords

[No Author keywords available]

Indexed keywords

4 AMINOBUTYRIC ACID A RECEPTOR; BENZODIAZEPINE DERIVATIVE; BENZODIAZEPINE RECEPTOR;

AFFECTIVE NEUROSIS; ARTICLE; CORRELATION FUNCTION; DRUG POTENCY; DRUG RECEPTOR BINDING; NERVE CELL NETWORK; RECEPTOR AFFINITY; STRUCTURE ACTIVITY RELATION;

ALGORITHMS; BENZODIAZEPINES; MODELS, GENETIC; NERVE NET; PROTEIN BINDING; RECEPTORS, GABA-A; STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 0030472786     PISSN: 00222623     EISSN: None     Source Type: Journal    
DOI: 10.1021/jm960536o     Document Type: Article

References (39)

1. So, S.-S.; Karplus, M. Evolutionary optimization in quantitative structure-activity relationship: an application of genetic neural network. J. Med. Chem. 1998, 39, 1521-1530.
2. Selwood, D. L.; Livingstone, D. J.; Comley, J. C.; Hudson, A. T.; Jackson, P.; Jandu, K. S.; Rose, V. S.; Stables, J. N. Structure-activity relationships of antifilarial antimycin analogues: a multivariate pattern recognition study. J. Med. Chem. 1990, 33, 136-142.
3. Rogers, D. R.; Hopfinger, A. J. Application of genetic function approximation to quantitative structure-activity relationships and quantitative structure-property relationships. J. Chem. Inf. Comput. Sci. 1994, 34, 854-866.
4. 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.
5. Wikel, J. H.; Dow, E. R. The use of neural networks for variable selection in QSAR. Bioorg. Med. Chem. Lett. 1993, 3, 645-651.
6. Möhler, H.; Okada, T. Benzodiazepine receptor: demonstration in the central nervous system. Science 1977, 198, 849-851.
7. Costa, T.; Rodbard, D.; Pert, C. B. Is the benzodiazepine receptor coupled to a choride anion channel? Nature 1979, 277, 315-317.
8. Tallman, J. F.; Paul, S. M.; Skolnick, P.; Gallager, D. W. Receptor for the age of anixety: pharmacology of the benzodiazepines. Science 1980, 207, 274-281.
9. Borea, P. A.; Bonora, A. Brain receptor binding and lipophilic character of benzodiazepines. Biochem. Pharmacol. 1983, 32, 603-607.
10. Haefely, W.; Kyburz, E.; Gerecke, M.; Möhler, H. Recent advances in the molecular pharmacology of benzodiazepine receptors and in the structure-activity relationships of their agonists and antagonists. Adv. Drug Res. 1985, 14, 165-322.
11. Villar, H. O.; Uyeno, E. T.; Toll, L.; Polgar, W.; Davies, M. F.; Loew, G. H. Molecular determinants of benzodiazepine receptor affinities and anticonvulsant activities. Mol. Pharmacol. 1989, 36, 589-600.
12. A receptor subunit for benzodiazepine pharmacology. Nature 1989, 338, 582-585.
13. Bianucci, A. M.; Da Settimo, A.; Da Settimo, F.; Primofiore, G.; Martini, G.; Giannaccini, G.; Lucacchini, A. Benzodiazepine receptor affinity and interaction of some N-(Indo-3-ylglyoxylyl)-amine derivatives. J. Med. Chem. 1992, 1992, 2214-2220.
14. Wong, G.; Koehler, K. F.; Skolnick, P.; Gu, Z.-Q.; Ananthan, S.; Schönholzer, P.; Hunkeleer, W.; Zhang, W.; Cook, J. M. Synthetic and computer-assisted analysis of the structural requirements for selective, high-affinity ligand binding to diazepam-insensitive benzodiazepine receptors. J. Med. Chem. 1993, 36, 1820-1830.
15. Bunin, B. A.; Ellman, J. A. A general and expedient method for solid-phase synthesis of 1,4-benzodiazepine derivatives. J. Am. Chem. Soc. 1992, 114, 10997-10998.
16. Plunkett, M. J.; Ellman, J. A. Solid-phase synthesis of structurally diverse 1,4-benzodiazepine derivatives using the Stille coupling reaction. J. Am. Chem. Soc. 1995, 117, 3306-3307.
17. Loew, G. H.; Nienow, J. R.; Poulsen, M. Theoretical structure-activity studies of benzodiazepine analogues. Mol. Pharmacol. 1984, 26, 19-34.
18. Ghose, A. K.; Crippen, G. M. Modeling the benzodiazepine receptor binding site by the general three-dimensional structure-directed quantitative structure-activity relationship method REMOTEDISC. Mol. Pharmacol. 1990, 37, 725-734.
19. Greco, G.; Novellino, E.; Silipo, C.; Vittoria, A. Study of benzodiazepines receptor sites using a combined QSAR-CoMFA approach. Quant. Struct.-Act. Relat. 1992, 11, 461-477.
20. Maddalena, D. J.; Johnston, G. A. R. Prediction of receptor properties and binding affinity of ligands to benzodiazepine/ GABAA receptors using artificial neural networks. J. Med. Chem. 1995, 38, 715-724.
21. They also introduced a nonphysical random variable as a descriptor for each substituent position. These random data input sets were not chosen in their final model.
22. Hansch, C.; Leo, A. A substituent constants for correlation analysis in chemistry and biology; John Wiley & Sons, Inc.: New York, 1979.
23. Lien, E. J.; Guo, Z.-R.; Li, R.-L.; Su, C.-T. Use of dipole moment as a parameter in drug-receptor interaction and quantitative structure-activity relationship studies. J. Pharm. Sci. 1982, 71, 641-655.
24. Rumelhart, D. E.; Hinton, G. E.; Williams, R. J. Learning internal representations by error propagation; Parallel distributed processing Vol. 1; MIT Press: Cambridge, MA, 1986.
25. Hertz, J.; Krogh, A.; Palmer, R. G. Introduction to the theory of neural computation; Addison-Wesley Publishing Co.: Redwood City, CA, 1991.
26. Møller, M. F. A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks 1993, 6, 525-533.
27. Chandonia, J.-M.; Karplus, M. The importance of larger data sets for protein secondary structure prediction with neural networks. Protein Sci. 1996, 5, 768-774.
28. Holland, J. H. Adaption in natural and artificial systems; The University of Michigan Press: Ann Arbor, MI, 1975.
29. Michalewicz, Z. Genetic algorithms + Data structures = Evolution Programs, Ist ed.; Artificial Intelligence Vol. XVII; Springer-Verlag: Berlin, 1992.
30. Cartwright, H. M. Applications of artificial intelligence in chemistry; Oxford University Press: Oxford, 1993.
31. A number of computers with different speeds have been used to perform calculations in this study. The time measurements given in the text correspond to either real CPU time or one that has been appropriately scaled for a Hewlett-Packard 735/125 work-station.
32. The number of new generations was 50 plus zeroth generation pool = 51, and there were 200 individuals. The sampling size was thus 200 × 51 = 10 200.
33. The total number of distinct 10-descriptor models chosen from a set of 42 descriptors is 42!/10!(42-10)! = 1 471 442 973.
34. Blount, J. F.; Fryer, R. I.; Gilman, N. W.; Todaro, L. J. Quinazolines and 1,4-benzodiazepines. 92. Conformational recognition of the receptor by 1,4-benzodiazepines. Mol. Pharmacol. 1983, 24, 425-428.
35. van de Waterbeemd, H. Chemometric methods in Molecular Design; Methods and principles in medicinal chemistry Vol. 2; VCH: Weinheim, 1995.
36. Topliss, J. G.; Edwards, R. P. Chance factors in studies of quantitative structure-activity relationships. J. Med. Chem. 1979, 22, 1238-1244.
37. Livingstone, D. J.; Manallack, D. T. Statistics using neural networks: Chance effects. J. Med. Chem. 1993, 36, 1295-1297.
38. 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.
39. 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.