Compound Library Design for Target Families

Methods in Molecular Biology

Volumn 575, Issue , 2009, Pages 21-46

  Balakin, K V ^a   Ivanenkov, Y A ^a   Savchuk, N P ^a  

^a NONE

Author keywords

Biological target; Chemogenomics; Chemokine; Compound library; GPCR; Kohonen self organized maps; Neural modeling; Receptor

Indexed keywords

CHEMOKINE RECEPTOR; G PROTEIN COUPLED RECEPTOR;

ANIMAL; ARTIFICIAL NEURAL NETWORK; CHEMISTRY; COMPUTER PROGRAM; DRUG DESIGN; DRUG DEVELOPMENT; DRUG EFFECT; FACTUAL DATABASE; GENETICS; GENOMICS; HUMAN; METHODOLOGY; MOLECULAR BIOLOGY; MOUSE; REVIEW;

ANIMALS; DATABASES, FACTUAL; DRUG DESIGN; DRUG DISCOVERY; GENOMICS; HUMANS; MICE; MOLECULAR BIOLOGY; NEURAL NETWORKS (COMPUTER); RECEPTORS, CHEMOKINE; RECEPTORS, G-PROTEIN-COUPLED; SOFTWARE;

MLCS; MLOWN;

EID: 75549084992     PISSN: 10643745     EISSN: 19406029     Source Type: Book Series    
DOI: 10.1007/978-1-60761-274-2_2     Document Type: Chapter

References (45)

1. http://www.prous.com.
2. Olah, M., Mracec, M., Ostopovici, L., Rad, R., Bora, A., Hadaruga, N., Olah, I., Banda, M., Simon, Z., Mracec, M., and Oprea, T. I. (2004) WOMBAT: World of molecular bioac-tivity. In: Oprea, T. I. (ed.) Cheminformatics in Drug Discovery. Wiley-VCH, Weinheim, pp. 223–239.
3. Vanco, J. (2003) The Beilstein CrossFire Information System and its use in pharma-ceutical chemistry. Ceska Slov. Farm. 52, 68–72.
4. Ivanenkov, Y. A., Balakin, K. V., Skorenko, A. V., Tkachenko, S. E., Savchuk, N. P., Ivachtchenko, A. A., and Nikolsky Y. (2003) Application of advanced machine learning algorithm for profiling specific GPCR-active compounds. Chem. Today 21, 72–75.
5. http://www.cis.hut.fi.
6. http://www.geocities.com.
7. http://www.cis.hut.fi.
8. http://www.informagenesis.com.
9. http://www.nd.com.
10. http://www.neurok.ru.
11. Todeschini, R., Consonni, V., Mannhold, R., Kubinyi, H., and Timmerman, H. (2000) Handbook of Molecular Descriptors. Wiley, New York.
12. Kohonen, T. (1990) The self-organizing map. Proceedings of the IEEE 78, 1464–1480.
13. Anzali, S., Gasteiger, J., Holzgrabe, U., Polanski, J., Sadowski, J., Teckentrup, A., and Wagener, M. (1998) The use of self-organizing neural networks in drug design. In: Kubinyi, H., Folkers, G., and Martin, Y. C. (eds.) 3D QSAR in Drug Design. Klu-wer/ESCOM, Dordrecht, pp. 273–99.
14. Bauknecht, H., Zell, A., Bayer, H., Levi, P., Wagener, M., Sadowski, J., and Gasteiger, J. (1996) Locating biologically active compounds in medium-sized heterogeneous datasets by topological autocorrelation vectors: Dopamine and benzodiazepine agonists. J. Chem. Inf. Comput. Sci. 36, 1205–1213.
15. Anzali, S., Barnickel, G., Krug, M., Sad-owski, J., Wagener, M., Gasteiger, J., and Polanski, J. (1996) The comparison of geo-metric 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, 521–534.
16. Brűstle, M., Beck, B., Schindler, T., King, W., Mitchell, T., and Clark, T. (2002) Descrip-tors, physical properties, and drug-likeness. J. Med. Chem. 45, 3345–3355.
17. Rabow, A. A., Shoemaker, R. H., Sausville, E. A., and Covell, D. G. (2002) Mining the National Cancer Institute’s tumor-screening database: Identification of compounds with similar cellular activities. J. Med. Chem. 45, 818–840.
18. Korolev, D., Balakin, K. V., Nikolsky, Y., Kirillov, E., Ivanenkov, Y. A., Savchuk, N. P., Ivashchenko, A. A., and Nikolskaya, T. (2003) Modeling of human cytochrome P450-mediated drug metabolism using unsupervised machine learning approach. J. Med. Chem. 46, 3631–3643.
19. Savchuk, N. P. (2003) In silico ADME-Tox as part of an optimization strategy. Curr. Drug Discov. 4, 17–22.
20. Kier, L. B., and Hall, L. H. (1986) Molecular Connectivity in Structure-Activity Analy-sis. Wiley, New York.
21. Basak, S. C., Balaban, A. T., Grunwald, G. D., and Gute, B. D. (2000) Topological indices: Their nature and mutual relatedness. J. Chem. Inf. Comput. Sci. 40, 891–898.
22. Bonchev, F. (2000) Overall connectivities/ topological complexities: A new powerful tool for QSPR/QSAR. J. Chem. Inf. Com-put. Sci. 40, 934–941.
23. Kubinyi, H. (1993) QSAR. Hansch Analysis and Related Approaches. In: Manhold, R., Krogs-gaard-Larsen, P., and Timmermann, H. (eds.) Methods and Principles in Medicinal Chemistry, vol. 1. VCH, Weinheim, pp. 21–36.
24. Livingstone, D. J. (2000) The characterization of chemical structures using molecular properties. a survey. J. Chem. Inf. Comput. Sci. 40, 195–209.
25. Jolliffe, I. T. (1986) Principal Component Analysis. Springer-Verlag, New York.
26. Cooley, W., and Lohnes, P. (1971) Multi-variate Data Analysis. Wiley, New York.
27. Clark, D. E., and Pickett, S. D. (2000) Computational methods for the prediction of ‘drug-likeness’. Drug Discov. Today 5, 49–58.
28. Oprea, T. I., Davis, A. M., Teague, S.J., and Lee-son, P. D. (2001) Is there a difference between leads and drugs? A historical perspective. J. Chem. Inf. Comput. Sci. 41, 1308–1315.
29. Klabunde, T. (2006) Chemogenomic approaches to ligand design. In: Rognan, D. (ed.) Ligand Design for G-protein-Coupled Receptors. Wiley-VCH, Wein-heim, pp. 115–135.
30. Rognan, D. (2007) Chemogenomic approaches to rational drug design. Br. J. Pharma-col. 152, 38–52.
31. Zlotnik, A., and Yoshie, O. (2000) Chem-okines: A new classification system and their role in immunity. Immunity 12, 121–127.
32. Yoshie, O., Imai, T., and Nomiyama, H. (2001) Chemokines in immunity. Adv. Immunol. 78, 57–110.
33. Balakin, K. V., Ivanenkov, Y. A., Tkachenko, S. E., Kiselyov, A. S., and Ivachtchenko, A. V. (2008) Regulators of chemokine receptor activity as promising anticancer therapeutics. Curr. Cancer Drug Targets 8, 299–340.
34. Zlotnik, A., Yoshie, O., and Nomiyama, H. (2006) The chemokine and chemokine receptor superfamilies and their molecular evolution. Genome Biol. 7, 243.
35. Allavena, P., Marchesi, F., and Mantovani, A. (2005) The role of chemokines and their receptors in tumor progression and invasion: Potential new targets of biological therapy. Curr. Cancer Ther. Rev. 1, 81–92.
36. Pérez-Nueno, V. I., Ritchie, D. W., Rabal, O., Pascual, R., Borrell, J. I., and Teixidó, J. (2008) Comparison of ligand-based and receptor-based virtual screening of HIV entry inhibitors for the CXCR4 and CCR5 receptors using 3D ligand shape matching and ligand-receptor docking. J. Chem. Inf. Model. 48, 509–533.
37. Spencer, E. H. (2005) Development of a Structure Prediction Method for G-Protein Coupled Receptors, Thesis. California Institute of Technology, Pasadena, CA.
38. Efremov, R., Truong, M. J., Darcissac, E. C., Zeng, J., Grau, O., Vergoten, G., Debard, C., Capron, A., and Bahr, G. M. (2001) Human chemokine receptors CCR5, CCR3 and CCR2B share common polarity motif in the first extracel-lular loop with other human G-protein coupled receptors. Eur. J. Biochem. 263, 746–756.
39. http://www.ChemDiv.com.
40. Torgerson, W. S. (1952) Multi-dimen-sional scaling: I. Theory and method. Psy-chometrika 17, 401–419.
41. Kruskal, J. B. (1964) Non-metric multi-dimensional scaling: A numerical method. Psychometrika 29, 115–129.
42. Goldberg, D. E. (1989) Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading.
43. Bigus, J. P. (1996) Data Mining with Neural Networks. McGraw-Hill, New York.
44. Kubinyi, H. (1994) Variable selection in QSAR studies. I. An evolutionary algorithm. Quant. Struct.-Act. Relat. 13, 285–294.1.
45. Lipinski, C.A., Lombardo, F., Dominy, B.W., and Feeney, P.J. (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug. Deliv. Rev. 23, 3–25.