Comparison of Support Vector Machine and Artificial Neural Network Systems for Drug/Nondrug Classification

Journal of Chemical Information and Computer Sciences

Volumn 43, Issue 6, 2003, Pages 1882-1889

  Byvatov, Evgeny ^a   Fechner, Uli ^a   Sadowski, Jens ^b   Schneider, Gisbert ^a  

^a GOETHE UNIVERSITY   (Germany)

^b ASTRAZENECA R AND D   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER SOFTWARE; NEURAL NETWORKS; TOPOLOGY; VECTORS;

BINARY DECISION PROBLEMS; SUPPORT VECTOR MACHINES (SVM);

DRUG PRODUCTS;

DRUG;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; BIOLOGY; CHEMICAL STRUCTURE; CLASSIFICATION; COMPARATIVE STUDY; COMPUTER PROGRAM; COMPUTER SYSTEM; FORECASTING; NOMENCLATURE; REPRODUCIBILITY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTATIONAL BIOLOGY; COMPUTER SYSTEMS; FORECASTING; MODELS, MOLECULAR; NEURAL NETWORKS (COMPUTER); PHARMACEUTICAL PREPARATIONS; REPRODUCIBILITY OF RESULTS; SOFTWARE; TERMINOLOGY;

EID: 0345548661     PISSN: 00952338     EISSN: None     Source Type: Journal    
DOI: 10.1021/ci0341161     Document Type: Article

References (40)

1. Clark, D. E.; Pickett, S. D. Computational methods for the prediction of 'drug-likeness'. Drug Discov. Today 2000, 5, 49-58.
2. Schneider, G.; Böhm, H.-J. Virtual screening and fast automated docking methods. Drug Discov. Today 2002, 7, 64-70.
3. Wold, S. Exponentially weighted moving principal component analysis and projections to latent structures. Chemomet. Intell. Lab. Syst. 1994, 23, 149-161.
4. Forina, M.; Casolino, M. C.; de la Pezuela Martinez, C. Multivariate calibration: applications to pharmaceutical analysis. J. Pharm. Biomed. Anal. 1998, 18, 21-33.
5. Neural Networks in QSAR and Drug Design; Devillers, J., Ed.; Academic Press: London, 1996.
6. Ajay; Walters, W. P.; Murcko, M. A. Can we learn to distinguish between "drug-like" and "nondrug-like" molecules? J. Med. Chem. 1998, 41, 3314-3324.
7. Sadowski, J.; Kubinyi, H. A scoring scheme for discriminating between drugs and nondrugs. J. Med. Chem. 1998, 41, 3325-3329.
8. Sadowski, J. Optimization of chemical libraries by neural networks. Curr. Opin. Chem. Biol. 2000, 4, 280-282.
9. Schneider, G. Neural networks are useful tools for drug design. Neural Networks 2000, 13, 15-16.
10. Sadowski, J. In Virtual Screening for Bioactive Molecules; Böhm, H.-J., Schneider, G., Eds.; Weinheim: Wiley-VCH: 2000; pp 117-129.
11. Cortes, C.; Vapnik, V. Support-vector networks. Machine Learning 1995, 20, 273-297.
12. Vapnik, V. The Nature of Statistical Learning Theory; Berlin: Springer, 1995.
13. Burbidge, R.; Trotter, M.; Buxton, B.; Holden, S. Drug design by machine learning: support vector machines for pharmaceutical data analysis. Comput. Chem. 2001, 26, 5-14.
14. Warmuth, M. K.; Liao, J.; Ratsch, G.; Mathieson, M.; Putta, S.; Lemmen, C. Active learning with Support Vector Machines in the drug discovery process. J. Chem. Inf. Comput. Sci. 2003, 43, 667-673.
15. Wilton, D.; Willett, P.; Lawson, K.; Mullier, G. Comparison of ranking methods for virtual screening in lead-discovery programs. J. Chem. Inf. Comput. Sci. 2003, 43, 469-474.
16. Todeschini, R.; Consonni, V. Handbook of Molecular Descriptors; Weinheim: Wiley-VCH: 2000.
17. Ghose, A. K.; Crippen, G. M. Atomic physicoehemical parameters for three-dimensional structure-directed quantitative structure-activity relationships 1. Partition coefficients as a Measure of hydrophobicity. J. Comput. Chem. 1986, 7, 565-577.
18. Ghose, A. K.; Crippen, G. M. Atomic physicoehemical parameters for three-dimensional structure-directed quantitative structure-activity relationships 2. Modeling dispersive and hydrophobic interactions. J. Comput. Chem. 1987, 27, 21-35.
19. Ghose, A. K.; Pritchett, A.; Crippen, G. M. Atomic physicochemical parameters for three-dimensional structure-directed quantitative structure-activity relationships 3. J. Comput. Chem. 1988, 9, 80-90.
20. Schneider, G.; Neidhart, W.; Giller, T.; Schmid, G. "Scaffold-hopping" by topological pharmacophore search: a contribution to virtual screening. Angew. Chem., Int. Ed. Engl. 1999, 38, 2894-2896.
21. Gasteiger, J.; Rudolph, C.; Sadowski, J. Automatic generation of 3D-atomic coordinates for organic molecules. Tetrahedron Comput. Methods 1990, 3, 537-547.
22. Coleman, T. F.; Li, Y. A reflective Newton method for minimizing a quadratic function subject to bounds on some of the variables. SIAM J. Optimization 1996, 6, 1040-1058.
23. Joachims, T. In Making large-scale SVM learning practical. Advances in Kernel Methods - Support Vector Learning; Schölkopf, B., Burges, C., Smola, A., Eds.; MIT-Press: Cambridge, MA, 1999; pp 41-56.
24. Cristianini, N.; Shawe-Taylor, J. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods; Cambridge University Press: Cambridge, 2000.
25. Burges, C. J. C. A tutorial on support vector machines for pattern recognition. Data Mining Knowledge Discovery 1998, 2, 121-167.
26. Bishop, C. M. Neural Networks for Pattern Recognition; Oxford: Oxford University Press: 1995.
27. Joachims, T. Learning to classify text using Support Vector Machines. Kluwer International Series in Engineering and Computer Science 668; Kluwer Academic Publishers: Boston, 2002.
28. Duda, R. O.; Hart, P. E.; Stork, D. G. Pattern Classification; Wiley-Interscience: New York, 2000.
29. Rumelhart, D. E.; McClelland, J. L.; The PDB Research Group. Parallel Distributed Processing; MIT Press: Cambridge, MA, 1986.
30. Hagan, M. T.; Demuth, H. B.; Beale, M. H. Neural Network Design; PWS Publishing: Boston, 1996.
31. Fletcher, R.; Reeves, C. M. Function minimization by conjugate gradients. Comput. J. 1964, 7, 149-154.
32. Moller, M. F. A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks 1993, 6, 525-533.
33. Dennis, J. E.; Schnabel, R. B. Numerical Methods for Unconstrained Optimization and Nonlinear Equations; Prentice-Hall: Englewood Cliffs, 1983.
34. Hagan, M. T.; Menhaj, M. Training feedforward networks with the Marquardt algorithm. IEEE Trans. Neural Networks 1994, 5, 989-993.
35. Foresee, F. D.; Hagan, M. T. Gauss-Newton approximation to Bayesian regularization. Proceedings of the 1997 International Joint Conference on Neural Networks; pp 1930-1935.
36. MacKay, D. J. C. Bayesian interpolation. Neural Comput. 1992, 4, 415-447.
37. Matthews, B. W. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim. Biophys. Acta 1975, 405, 442-451.
38. Krogh, A.; Sollich, P. Statistical mechanics of ensemble learning. Phys. Rev. E 1997, 55, 811-825.
39. Baldi, P.; Brunak, S. Bioinformatics - The Machine Learning Approach; MIT Press: Cambridge, 1998.
40. Byvatov, E.; Schneider, G. Support vector machine applications in bio-informatics. Appl. Bioinf. 2003, 2, 67-77.