Advanced Artificial Intelligence Methods Used in the Design of Pharmaceutical Agents

Pharmaceutical Data Mining: Approaches and Applications for Drug Discovery

Volumn , Issue , 2009, Pages 457-489

  Ivanenkov, Yan A ^a,b   Khandarova, Ludmila M ^c  

^a Chemical Diversity Research Institute   (Russian Federation)

^b INSTITUTE OF PHYSIOLOGICALLY ACTIVE COMPOUNDS   (Russian Federation)

^c InformaGenesis Ltd   (Russian Federation)

Author keywords

Advanced artificial intelligence methods in pharmaceutical agent design; Advanced computational techniques; Mapping software InformaGenesis, software based on Kohonen SOM algorithm

Indexed keywords

EID: 84890997469     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/9780470567623.ch16     Document Type: Chapter

References (60)

1. Ekins S, Mestres J, Testa B. In silico pharmacology for drug discovery: Applications to targets and beyond. Br J Pharmacol 2007; 152: 21-37.
2. Ekins S, Mestres J, Testa B. In silico pharmacology for drug discovery: Methods for virtual ligand screening and profi ling. Br J Pharmacol 2007; 152: 9-20.
3. Balakin KV, Kozintsev AV, Kiselyov AS, Savchuk NP. Rational design approaches to chemical libraries for hit identifi cation. Curr Drug Discov Technol 2006; 3: 49 - 65.
4. Bernard P, Golbraikh A, Kireev D, Chretien JR, Rozhkova N. Comparison of chemical databases: Analysis of molecular diversity with self organizing maps (SOM). Analysis 1998; 26: 333-341.
5. Agrafiotis DK. Stochastic algorithms for maximizing molecular diversity. J Chem Inf Comput Sci 1997; 37: 841-851.
6. Bayada DM, Hamersma H, van Geerestein VJ. Molecular diversity and representativity in chemical databases. J Chem Inf Comput Sci 1999; 39: 1-10.
7. Kirew DB, Chretien JR, Bernard P, Ros F. Application of Kohonen neural networks in classifi cation of biologically active compounds. SAR QSAR Environ Res 1998; 8: 93-107.
8. Ros F, Audouze K, Pintore M, Chrétien JR. Hybrid systems for virtual screening: Interest of fuzzy clustering applied to olfaction. SAR QSAR Environ Res 2000; 11: 281-300.
9. Balakin K. Pharma ex machina. Mod Drug Discov 2003; 8: 45-47.
10. Oprea TI. Chemical space navigation in lead discovery. Curr Opin Chem Biol 2002 ; 6: 384-389.
11. Oprea TI, Gottfries J. Chemography: The art of navigating in chemical space. J Comb Chem 2001; 3: 157-166.
12. Johnson MA, Maggiora GM. Concepts and Applications of Molecular Similarity. New York: Wiley, 1990.
13. Sammon JW. A non-linear mapping for data structure analysis. IEEE Trans Comput 1969; C-18: 401-409.
14. Chang CL, Lee RCT. A heuristic relation method for nonlinear mapping in the cluster analysis. IEEE Trans Syst Man Cybern 1973; SMC-3: 197-200.
15. Pykett CE. Improving the effi ciency of Sammon' s non-linear mapping by using clustering archetypes. Electron Lett 1978; 14: 799-800.
16. Lee RCY, Slagle JR, Blum H. A triangulation method for the sequential mapping of points from N-space to two space. IEEE Trans Comput 1977; C-27: 288 - 292.
17. Biswas G, Jain AK, Dubes RC. Evaluation of projection algorithms. IEEE Trans Pattern Anal Mach Intell 1981; PAMI-3: 701-708.
18. Sadowski J, Wagener M, Gasteiger J. Assessing similarity and diversity of combinatorial libraries by spatial autocorrelation functions and neural networks. Angew Chem Int Ed Engl 1995; 34: 2674-2677.
19. Jiang J-H, Wang J-H, Liang Y-Z, Yu R-Q. A non-linear mapping-based generalized backpropagation network for unsupervised learning. J Chemom 1996; 10: 241 - 252.
20. Agrafiotis DK, Lobanov VS. Nonlinear mapping networks. J Chem Inf Comput Sci 2000; 40: 1356-1362.
21. Agrafiotis DK. A new method for analyzing protein sequence relationships based on Sammon maps. Protein Sci 1997; 6: 287-293.
22. Balakin KV, Ivanenkov YA, Savchuk NP, Ivashchenko AA, Ekins S. Comprehensive computational assessment of ADME properties using mapping techniques. Curr Drug Discov Technol 2005; 2: 99-113.
23. Willshaw DJ, von der Malsburg C. How patterned neural connections can be set by self-organization. Proc R Soc Lond B Biol Sci 1976; 194: 431-445.
24. Kohonen T. Self-Organization and Associative Memory, 3rd edn. New York: Springer-Verlag, 1988.
25. Kohonen T. The self- organizing map. Proc IEEE 1990; 78: 1464-1480.
26. Kohonen T. Physiological interpretation of the self-organizing map algorithm. Neural Netw 1993; 6: 895-905.
27. Linde Y, Buzo A, Gray RM. An algorithm for vector quantizer design. IEEE Trans Commun 1980; 28: 84-95.
28. Kohonen T. Self-Organizing Maps. Heidelberg: Springer-Verlag, 1996.
29. Anzali S, Gasteiger J, Holzgrabe U, Polanski J, Sadowski J, Teckentrup A, Wagener A. The use of self-organizing neural networks in drug design. In: 3D QSAR in Drug Design, edited by Kubinyi H, Folkers G, Martin YC, pp. 273-299. Dordrecht, The Netherlands: Kluwer/ESCOM, 1998.
30. Balakin KV, Ivanenkov YA, Skorenko AV, Nikolsky YV, Savchuk NP, Ivashchenko AA. In silico estimation of DMSO solubility of organic compounds for bioscreening . J Biomol Screen 2004; 9: 22-31.
31. Korolev D, Balakin KV, Nikolsky Y, Kirillov E, Ivanenkov YA, Savchuk NP, Ivashchenko AA, Nikolskaya T. Modeling of human cytochrome P450-mediated drug metabolism using unsupervised machine learning approach. J Med Chem 2003; 46: 3631-3643.
32. Kohonen T. Self-organized formation of topologically correct feature maps. Biol Cybern 1982; 43: 59-69.
33. Kohonen T. Self-organizing maps: Optimization approaches. Artif Neural Netw 1991; 2: 981-990.
34. Koikkalainen P. Tree structured self-organizing maps. In: Kohonen Maps, edited by Oja E, Kaski S, pp. 121-130. Amsterdam: Elsevier, 1999.
35. Kangas J, Kohonen T, Laaksonen J. Variants of the self-organizing map. IEEE Trans Neural Netw 1990; 1: 93-99.
36. Martinetz TM, Berkovich SG, Schulten KJ. "Neural gas" network for vector quantization and its application to time series prediction. IEEE Trans Neural Netw 1993; 4: 558-569.
37. DeSieno D. Adding a conscience to competitive learning. In: Proceedings of the ICNN' 88, International Conference on Neural Networks, pp. 117-124. Piscataway, NJ: IEEE Service Center, 1988.
38. Fritzke B. Growing cell structures - A self-organizing neural network for unsupervised and supervised learning. Neural Netw 1994; 7: 1441-1460.
39. Bishop C, Svensen M, Williams C. GTM: The generative topographic mapping. Neural Comput 1998; 10: 215-234.
40. Tenenbaum JB. Mapping a manifold of perceptual observations. In: Advances in Neural Information Processing Systems, edited by Jordan M, Kearns M, Solla S, Vol. 10, pp. 682-688. Cambridge, MA: The MIT Press, 1998.
41. Balasubramanian M, Schwartz EL. The Isomap algorithm and topological stability. Science 2002; 295: 7.
42. Lee JA, Verleysen M. Nonlinear dimensionality reduction of data manifolds with essential loops. Neurocomputing 2005; 67: 29-53.
43. Choi H, Choi S. Robust kernel Isomap. Pattern Recognit 2007; 40: 853-862.
44. Saxena A, Gupta A, Mukerjee A. Non-linear dimensionality reduction by locally linear isomaps. Lect Notes Comput Sci 2004; 3316: 1038-1043.
45. Tenenbaum JB, de Silva V, Langford JC. A global geometric framework for nonlinear dimensionality reduction. Science 2000; 290: 2319-2323.
46. Lim IS, Ciechomski PH, Sarni S, Thalmann D. Planar arrangement of high - dimensional biomedical data sets by Isomap coordinates. In: Proceedings of the 16th IEEE Symposium on Computer-Based Medical Systems, pp. 50-55. Newton, MA: Butterworth-Heinemann, 2003.
47. Dawson K, Rodriguez RL, Malyj W. Sample phenotype clusters in high-density oligonucleotide microarray data sets are revealed using Isomap, a nonlinear algorithm . BMC Bioinformatics 2005; 6: 1-17.
48. Borg I, Groenen PJF. Modern Multidimensional Scaling: Theory and Applications. New York: Springer, 1997.
49. Roweis ST, Saul LK. Nonlinear dimensionality reduction by locally linear embedding . Science 2000; 290: 2323-2326.
50. Agrafiotis DK, Xu H. A self-organizing principle for learning nonlinear manifolds. Proc Natl Acad Sci USA 2002; 99: 15869-15872.
51. Agrafiotis DK, Xu H. A geodesic framework for analyzing. Molecular similarities. J Chem Inf Comput Sci 2003; 43: 475-484.
52. Rassokhin DN, Agrafiotis DK. A modifi ed update rule for stochastic proximity embedding. J Mol Graph Model 2003; 22: 133-140.
53. Spellmeyer DC, Wong AK, Bower MJ, Blaney JM. Conformational analysis using distance geometry methods. J Mol Graph Model 1997; 15: 18-36.
54. Havel TF, Wuthrich K. An evaluation of the combined use of nuclear magnetic resonance and distance geometry for the determination of protein conformations in solution. J Mol Biol 1985; 182: 281-294.
55. Zhu F, Agrafiotis DK. Self-organizing superimposition algorithm for conformational sampling. J Comput Chem 2007; 28: 1234-1239.
56. Xu H, Izrailev S, Agrafiotis DK. Conformational sampling by self-organization. J Chem Inf Comput Sci 2003; 43: 1186-1191.
57. Agrafiotis DK, Bandyopadhyay D, Carta G, Knox AJS, Lloyd DG. On the effects of permuted input on conformational sampling of drug-like molecules: An evaluation of stochastic proximity embedding. Chem Biol Drug Des 2007; 70: 123-133.
58. Farnum MA, Xu H, Agrafiotis DK. Exploring the nonlinear geometry of protein homology. Protein Sci 2003; 12: 1604-1612.
59. Demartines P, Herault J. Curvilinear component analysis: A self-organizing neural network for nonlinear mapping of data sets. IEEE Trans Neural Netw 1997; 8: 148-154.
60. Todeschini R, Consonni V. Handbook of Molecular Descriptors. New York: Wiley-VCH, 2002.