Supervised self-organizing maps in drug discovery. 1. Robust behavior with overdetermined data sets

Journal of Chemical Information and Modeling

Volumn 45, Issue 6, 2005, Pages 1749-1758

  Xiao, Yun De ^a   Clauset, Aaron ^b   Harris, Rebecca ^a   Bayram, Ersin ^c   Santago II, Peter ^c   Schmitt, Jeffrey D ^a  

^a TARGACEPT INC   (United States)

^b University of New Mexico   (United States)

^c VIRGINIA POLYTECHNIC INSTITUTE AND STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA ACQUISITION; GENETIC ALGORITHMS; REGRESSION ANALYSIS; SELF ORGANIZING MAPS; STATISTICAL METHODS; VECTOR QUANTIZATION;

CHEMOMETRY; DATASET; LINEAR REGRESSION; NONLINEAR METHODS;

DRUG THERAPY;

DIHYDROFOLATE REDUCTASE;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; CHEMICAL STRUCTURE; CHEMISTRY; CLUSTER ANALYSIS; CONFORMATION; DRUG DESIGN; FACTUAL DATABASE; NONLINEAR SYSTEM; PHARMACOLOGY; PREDICTION AND FORECASTING; QUANTITATIVE STRUCTURE ACTIVITY RELATION; STATISTICAL ANALYSIS; STATISTICAL MODEL; STATISTICS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CLUSTER ANALYSIS; DATA INTERPRETATION, STATISTICAL; DATABASES, FACTUAL; DRUG DESIGN; MODELS, MOLECULAR; MODELS, STATISTICAL; MOLECULAR CONFORMATION; NONLINEAR DYNAMICS; PHARMACOLOGY; PREDICTIVE VALUE OF TESTS; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; TETRAHYDROFOLATE DEHYDROGENASE;

EID: 28944437860     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci0500839     Document Type: Conference Paper

References (29)

1. Hansch, C. A Quantitative Approach to Biochemical Structure-Activity Relationships. Acc. Chem. Res. 1969, 2, 232-39.
2. Draper, N. R.; Smith, H. Applied Regression Analysis; John Wiley & Sons: New York, 1966; p 407.
3. Lindberg, W.; Persson, J.; Wold, S. Anal. Chem. 1983, 55, 643.
4. Geladi, P.; Kowalski, B. R. Partial Least Squares Regression: A Tutorial. Anal. Chim. Acta 1986, 185, 1-17.
5. Bellman, R. Adaptive Control Processes: A Guided Tour; Princeton University Press: Princeton, NJ, 1961.
6. Leardi, R.; Boggia, R.; Terrile, M. Genetic Algorithms as a Strategy for Feature Selection. J. Chemom. 1992, 6, 267-281.
7. Bayram, E.; Santago, P., II; Harris, R.; Xiao, Y.; Clauset, A. J.; Schmitt, J. D. Genetic algorithms and self-organizing maps: a powerful combination for modeling complex QSAR and QSPR problems. J. Comput.-Aided Mol. Des. 2004, 483-493.
8. Rogers, D.; 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, 4-866.
9. Gasteiger, J.; Zupan, J. Neural Networks in Chemistry. Angew. Chem., Int. Ed. Engl. 1993, 32, 503-527.
10. Anzali, S.; Gasteiger, J.; Holzgrabe, U.; Polanski, J.; Sadowski, J.; Tackentrup, A.; Wagener, M. The Use of Self-Organizing Neural Networks in Drug Design; Kubinyi, H., Folkers, G., Martin, Y. C., Eds.; Kluwer Academic: Boston, 1998; pp 273-99.
11. Kovalishyn, V. V.; Tetko, I. V.; Luik, A. I.; Ivakhnenko, A. G.; Livingstone, D. J. Application of Self-Organizing Neural Networks with Active Neurons for QSAR Studies. Proceedings of the European Symposium on Quantitative Structure-Activity Relationships: Molecular Modeling and Prediction of Bioactivity; 2000; Vol. 12, pp 444-45.
12. Larry, L.; Curt, B. The use of 2D, 3D, TAE and wavelet coefficient descriptors (WCDs) for generatingself-organizing Kohonen maps for QSAR, QSPR and ADME Analyses; ACS National Meeting, Washington, D.C., August 2000.
13. Rose, V. S.; Croall, I. F.; MacFie, H. J. H. An Application of Unsupervised Neural Network Methodology Kohonen Topology-Preserving Mapping to QSAR Analysis. Quant. Struct.-Act. Relat. 1991, 10(1), 6-15.
14. Polanski, J. Self-organizing neural network for modeling 3D QSAR of colchicinoids. Acta Biochim. Pol. 2000, 47 (1), 37-45.
15. Espinosa, G.; Arenas, A.; Giralt, F. An integrated SOM-fuzzy ARTMAP neural system for the evaluation of toxicity. J. Chem. Inf. Comput. Sci. 2002, 42, 343-59.
16. Zupan, J.; Gasteiger, J. Neural Network and Drug Design for Chemists, 2nd ed.; VCH: Weinheim, 1999.
17. a Values and Steroid CBG Binding Affinities, J. Chem. Inf. Comput. Sci. 2003, 43, 2081-2092.
18. Kohonen, T. The self-organizing map. Neurocomputing 1998, 21 (1-3), 1-6.
19. Kohonen, T. Self-Organizing Maps, 3rd ed.; Springer: Berlin, 2001.
20. Van der Putten, P. Utilizing the Topology Preserving Property of Self-Organizing Maps for Classification. M.Sc. Thesis, Cognitive Artificial Intelligence, Utrecht University, NL, 1996.
21. Anderberg, M. R. Cluster Analysis for Applications; Academic Press: New York, 1973.
22. Kaski, S. Data exploration using self-organizing maps. Acta Polytech. Scand., Math., Comput. Manage. Eng. Ser. No. 82 1997, 57.
23. Kohonen, T.; Makisara, K.; Saramaki, T. Phonotopic Maps - Insightful Representation of Phonological Features for Speech Recognition. Proceeding of the IEEE Seventh International Conference on Pattern Recognition; 1984; pp 182-185.
24. Matlab 6.5; The Mathworks, Inc.: 3 Apple Hill Dr., Natick, MA.
25. http://www.cis.hut.fi/projects/somtoolbox/.
26. Cover, T.; Hart, P. Nearest Neighbor Pattern Classification. IEEE Trans. Inf. Theory 1967, 13:21-27.
27. 2 Modeling Environment, Release 4.9, Accelrys Inc.: San Diego, 2003.
28. Silipo, C.; Hansch, C. Correlation Analysis. Its Application to the Structure-Activity Relationship of Triazines Inhibiting Dihydrofolate Reductase. J. Am. Chem. Soc. 1975, 97, 6849-6861.
29. QSARIS 1.1; MDL Information Systems Inc.