Application of Support Vector Machine-Based Ranking Strategies to Search for Target-Selective Compounds

Methods in Molecular Biology

Volumn 672, Issue , 2011, Pages 517-530

  Wassermann, Anne Mai ^a   Geppert, Hanna ^a   Bajorath, Jürgen ^a  

^a UNIVERSITY OF BONN   (Germany)

Author keywords

Kernels; Machine learning; Multi class SVM; Selectivity searching; Support vector machines; SVM preference ranking; Target selectivity

Indexed keywords

MACHINE LEARNING; MULTICLASS SUPPORT VECTOR MACHINE; RECALL; ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; CHEMISTRY; COMPUTER SIMULATION; FACTUAL DATABASE; MOLECULAR LIBRARY; QUANTITATIVE STRUCTURE ACTIVITY RELATION;

PROTEIN;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; DATABASES, FACTUAL; PROTEINS; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; SMALL MOLECULE LIBRARIES;

MLCS; MLOWN;

EID: 79952116048     PISSN: 10643745     EISSN: 19406029     Source Type: Book Series    
DOI: 10.1007/978-1-60761-839-3_21     Document Type: Chapter

References (19)

1. Bredel, M. and Jacoby, E. (2004) Chemogenomics: an emerging strategy for rapid target and drug discovery. Nat. Rev. Genet. 5, 262–275.
2. Stockwell, B. R. (2004) Exploring biology with small organic molecules. Nature 432, 846–854.
3. Bajorath, J. (2008) Computational analysis of ligand relationships within target families. Curr. Opin. Chem. Biol. 12, 352–358.
4. Stumpfe, D., Geppert, H., and Bajorath, J. (2008) Methods for computer-aided chemical biology, part 3: analysis of structure-selectivity relationships through single-or dual-step selectivity searching and Bayesian classification. Chem. Biol. Drug. Des. 71, 518–528.
5. Wassermann, A. M., Geppert, H., and Bajorath, J. (2009) Searching for target-selective compounds using different combinations of multiclass support vector machine ranking methods, kernel functions, and fingerprint descriptors. J. Chem. Inf. Model. 49, 582–592.
6. Burges, C. J. C. (1998) A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2, 121–167.
7. Vapnik, V. N. The Nature of Statistical Learning Theory, 2nd ed.; Springer: New York, 2000.
8. Boser, B. E., Guyon, I. M., and Vapnik, V. A training algorithm for optimal margin classifiers. In Proceedings of the 5th Annual Workshop on Computational Learning Theory, Pittsburgh, Pennsylvania, 1992; ACM: New York, 1992; pp 144–152.
9. Burbidge, R., Trotter, M., Buxton, B., and Holden, S. (2001) Drug design by machine learning: support vector machines for pharmaceutical data analysis. Comput. Chem. 26, 5–14.
10. Warmuth, M. K., Liao, J., R€atsch, G., Mathieson, M., Putta, S., and Lemmen, C. (2003) Active learning with support vector machines in the drug discovery process. J. Chem Inf. Comput. Sci. 43, 667–673.
11. Jorissen, R. N. and Gilson, M. K. (2005) Virtual screening of molecular databases using a support vector machine. J. Chem. Inf. Model. 45, 549–561.
12. Geppert, H., Horváth, T., G€artner, T., Wrobel, S., and Bajorath, J. (2008) Support-vector-machine-based ranking significantly improves the effectiveness of similarity searching using 2D fingerprints and multiple reference compounds. J. Chem. Inf. Model. 48, 742–746.
13. MACCS Structural Keys; Symyx Software: San Ramon, CA, 2005.
14. Joachims, T. Optimizing search engines using clickthrough data. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Alberta, Canada, 2002; ACM: New York, 2002; pp 133–142.
15. Powell, M. J. D. Radial basis functions for multivariable interpolation: a review. In Mason, J. C., and Cox, M. G. (eds). Algorithms for Approximation; Clarendon Press, Oxford: 1987; pp 143–167.
16. MDL Drug Data Report (MDDR); Symyx Software: San Ramon, CA, 2005.
17. MOE (Molecular Operating Environment); Chemical Computing Group Inc.: Montreal, Quebec, Canada, 2007.
18. Ralaivola, L., Swamidass, S. J., Saigo, H., and Baldi, P. (2005) Graph kernels for chemical informatics. Neural Netw. 18, 1093–1110.
19. Bender, A., Mussa, H. Y., Glen, R. C., and Reiling, S. (2004) Molecular similarity searching using atom environments, information-based feature selection, and a naïve Bayesian classifier. J. Chem. Inf. Comput. Sci. 44, 170–178.