Modeling and predicting binding affinity of phencyclidine-like compounds using machine learning methods

Journal of Chemometrics

Volumn 24, Issue 1, 2010, Pages 1-13

  Erdas, Ozlem ^a   Buyukbingol, Erdem ^b   Alpaslan, Ferda Nur ^a   Adejare, Adeboye ^c  

^a MIDDLE EAST TECHNICAL UNIVERSITY   (Turkey)

^b ANKARA UNIVERSITY   (Turkey)

^c UNIVERSITY OF THE SCIENCES   (United States)

Author keywords

Binding affinity prediction; Spherical self organizing maps; Support vector machines

Indexed keywords

BINDING ENERGY; CONFORMAL MAPPING; ELECTROSTATICS; LEAST SQUARES APPROXIMATIONS; SELF ORGANIZING MAPS; SUPPORT VECTOR MACHINES;

BINDING AFFINITIES; BINDING AFFINITY PREDICTION; ELECTROSTATIC POTENTIAL SURFACE; ENGINEERING FIELDS; MACHINE LEARNING METHODS; MOLECULAR ELECTROSTATIC POTENTIALS; SCIENCE AND ENGINEERING; SELF-ORGANIZING-MAPS; SPHERICAL SELF-ORGANIZING MAP; SUPPORT VECTORS MACHINE;

FORECASTING;

EID: 74349106273     PISSN: 08869383     EISSN: 1099128X     Source Type: Journal    
DOI: 10.1002/cem.1265     Document Type: Article

References (28)

1. Schneider G. Neural networks are useful tools for drug design. Neural Netw. 2000; 13: 15-16.
2. Wolters HJ. Geometric modeling applications in rational drug design: a survey. Comput. Aided. Geom. Des. 2006; 23: 482-494.
3. Bauknecht H, Zell A, Bayer H, Levi P, Wagener M, Sadowski J, Gasteiger J. Representation of Molecular Electrostatic Potentials by Topological Feature Maps. J. Chem. Inf. Comput. Sci. 1996; 36: 1205-1213.
4. Gasteiger J, Li X. Mapping the electrostatic potential of muscarinic and nicotinic agonists with artificial neural networks. Angew. Chem. Int. Ed. Engl. 1994; 33: 643-646.
5. Zell A, Bayer H, Bauknecht H. Similarity analysis of molecules with self-organizing surfaces-an extension of the self-organizing map. ICNN'94: International Conference on Neural Networks, 1994; 719-724.
6. Buyukbingol E, Sisman A, Akyildiz M, Alparslan F, Adejare A. Adaptive neuro-fuzzy inference system (ANFIS): a new approach to predictive modeling in QSAR applications: a study of neuro-fuzzy modeling of PCP-based NMDA receptor antagonists. Bioorg. Med. Chem. 2007; 15: 4265-4282.
7. HyperChem Version 5.1, Molecular Modeling Toolkit, HyperCube, Inc., http://www.hyper.com
8. VegaZZ Version 2.0.8, Molecular Modeling Toolkit, Drug Design Laboratory, http://www.vegazz.net
9. Kohonen T. The Self-Organizing Map. Proc. IEEE 1990; 78: 1464-1480.
10. Kangas J, Kohonen T, Laaksonen J. Variants of self-organizing maps. IEEE Trans. Neural Netw. 1990; 1: 93-99.
11. Kohonen T. Self-Organizing Maps. Springer: New York, 2001.
12. Sangole A, Knopf G. Geometric representations for high-dimensional data using a spherical SOFM. Int. J. Smart Eng. Syst. Des. 2003; 5: 11-20.
13. Wu Y, Takatsuka M. The Geodesic Self-Organizing Map and its error analysis. ACSC'05: Proceedings of the Twenty-eighth Australasian conference on Computer Science, Darlinghurst, Australia, 2005; 343-351.
14. Boudjemaï F, Enberg P, Postaire J-G. Surface modeling by using self organizing maps of Kohonen. IEEE International Conferenceon Systems, Man and Cybernetics, 2003; 2418-2423.
15. Leontitsis A, Sangole A. Estimating an optimal neighborhood size in the spherical self-organizing feature map. Int. J. Comput. Intell. 2005; 2(2): 94-98.
16. Sangole A, Knopf G. Visualization of randomly ordered numeric data sets using spherical self-organizing feature maps. Comput. Graph. 2003; 27: 963-976.
17. Kecman V. In Support Vector Machines-An Introduction, Vol. 177, Chapter 1, Wang L (ed.). Springer-Verlag: Berlin, Heidelberg, 2005; 1-49.
18. Üstün B. M.Sc. Thesis, Katholieke Universiteit Nijmegen, 2003.
19. Abdi H. Partial least squares (PLS) regression. In Encyclopedia for Research Methods for the Social Sciences, Lewis-Beck M, Bryman A, Futing T (eds). Sage, Thousand Oaks, CA, 2003; 792-795.
20. Chang C-C, Lin C-J. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm
21. Unscrambler Version 9.7, Multivariate Analysis and Experimental Design Software, CAMO Software, http://unscrambler.camo.com
22. Weisberg S. Applied Linear Regression (3rd edn). Wiley Series in Probability and Statistics; Wiley Hoboken, NJ, 2005.
23. Krishnamurthy A, Ahalt S, Melton D, Chen P. Neural networks for vector quantization of speech and images. IEEE J. Select. Areas Commun.1990; 8: 1449-1457.
24. Kamimura R. Competitive learning by information maximization: eliminating dead neurons in competitive learning, Vol. 2714. ICANN, Lecture Notes in Computer Science, Springer: Berlin, 2003; 99-106.
25. DeSieno D. Adding a conscience to competitive learning. IEEE International Conference on Neural Networks, Vol. 1., 1988; 117-124.
26. Cherkassky V, Ma Y. Practical selection of SVM parameters and noise estimation for SVM regression. Neural Netw. 2004; 17: 113-126.
27. Smola A, Schölkopf B. Atutorialon support vector regression. Technical Report, 1998.
28. Moro S, Bacilieri M, Cacciari B, Spalluto G. Autocorrelation of molecular electrostatic potential surface properties combined with partial least squares analysis as new strategy for the prediction of the activity of human A3 adenosine receptor antagonists. J. Med. Chem. 2005; 48: 5698-5704.