The pharmacophore kernel for virtual screening with support vector machines

Journal of Chemical Information and Modeling

Volumn 46, Issue 5, 2006, Pages 2003-2014

  Mahé, Pierre ^a   Ralaivola, Liva ^b   Stoven, Véronique ^a   Vert, Jean Philippe ^a  

^a PSL RESEARCH UNIVERSITY   (France)

^b AIX MARSEILLE UNIVERSITY   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATABASE SYSTEMS; DRUG PRODUCTS; LEARNING SYSTEMS; MOLECULAR DYNAMICS; MOLECULAR STRUCTURE; SCREENING;

FINGERPRINT-BASED APPROACHES; KERNEL METHODS; VIRTUAL SCREENING APPLICATIONS;

PHARMACODYNAMICS;

DRUG;

ALGORITHM; ARTICLE;

ALGORITHMS; PHARMACEUTICAL PREPARATIONS;

EID: 33750294461     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci060138m     Document Type: Article

References (41)

1. Manly, C.; Louise-May, S.; Hammer, J. The impact of informatics and computational chemistry on synthesis and screening. Drug Discovery Today 2001, 6 (21), 1101-1110.
2. Butina, D.; Segall, M. D.; Frankcombe, K. Predicting ADME properties in silico: Methods and models. Drug Discovery Today 2002, 7 (Suppl. 11), S83-S88.
3. Lipinski, C. A.; Lombarde, F.; dominy, B. W.; Feeney, P. J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug. Delivery Rev. 2001, 46 (1-3), 3-26.
4. King, R. D.; Muggleton, S. H.; Srinivasan, A.; Sternberg, M. J. E. Structure - activity relationships derived by machine learning: The use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming. Proc. Natl. Acad. Sci. U.S.A. 1996, 93 (1), 438-442.
5. Böhm, H. J.; Schneider, G.; Mannhold, R.; Kubinyi, H.; Folkers, G. Protein - Ligand Interactions; Wiley: New York, 2003.
6. Halperin, I.; Ma, B.; Wolfson, H.; Nussinov, R. Principles of docking: An overview of search algorithms and a guide to scoring functions. Proteins 2002, 47 (4), 409-443.
7. Lemmen, C.; Lengauer, T. Computational methods for the structural alignment of molecules. J. Comput.-Aided Mol. Des. 2000, 14 (3), 215-232.
8. Xue, L.; Bajorath, J. Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Comb. Chem. High. Throughput Screening 2000, 3 (5), 363-372.
9. Holliday, J. D.; Willett, P. Using a genetic algorithm to identify common structural features in sets of ligands. J. Mol. Graphics Modell. 1997, 15 (4), 221-232.
10. Finn, P.; Muggleton, S. H.; Page, D.; Srinivasan, A. Pharmacophore discovery using the inductive logic programming language Prog. Machine Learning 1998, 30 (2-3), 241-270.
11. Matter, H.; Pötter, T. Comparing 3D pharmacophore triplets and 2D fingerprints for selecting diverse compound subsets. J. Chem. Inf. Model. 1999, 39 (6), 1211-1225.
12. Brown, R. D.; Martin, Y. C. The information content of 2D and 3D structural descriptors relevant to ligand-receptor binding. J. Chem. Inf. Comput. Sci. 1997, 37 (1), 1-9.
13. Bajorath, J. Selected concepts and investigations in compound classification, molecular descriptor analysis, and virtual screening. J. Chem. Inf. Comput. Sci. 2001, 41 (2), 233-245.
14. Boser, B. E.; Guyon, I. M.; Vapnik, V. N. A training algorithm for optimal margin classifiers. In Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory; ACM Press: New York, 1992; pp 144-152.
15. Vapnik, V. N. Statistical Learning Theory; Wiley: New York, 1998.
16. Burbidge, R.; Trotter, M.; Buxton, B.; Holden, S. Drug design by machine learning: Support vector machines for pharmaceutical data analysis. Comput. Chem. 2001, 26 (1), 4-15.
17. Byvatov, E.; Fechner, U.; Sadowski, J.; Schneider, G. Comparison of support vector machine and artificial neural network systems for drug/nondrug classification. J. Chem. Inf. Comput. Sci. 2003, 43 (6), 1882-1889.
18. Mahé, P.; Ueda, N.; Akutsu, T.; Perret, J. L.; Yert, J. P. Graph kernels for molecular structure-activity relationship analysis with support vector machines. J. Chem. Inf. Model. 2005, 45 (4), 939-951.
19. Ralaivola, L.; Swamidass, S. J.; Saigo, H.; Baldi, P. Graph kernels for chemical informatics. Neural Networks 2005, 18 (8), 1093-1110.
20. Schölkopf, B.; Smola, A. J. Learning with Kernels: Support Vector Machines, Regularisation, Optimization, and Beyond; MIT Press: Cambridge, MA, 2002.
21. Kashima, H.; Tsuda, K.; Inokuchi, A. Kernels for graphs. In Kernel Methods in Computational Biology; Schölkopf, B., Tsuda, K., Vert, J. P., Eds.; MIT Press: Cambridge, MA, 2004; pp 155-170.
22. Burges, C. J. C. A tutorial on support vector machines for pattern recognition. Data Min. Knowledge Discovery 1998, 2 (2), 121-167.
23. Shawe-Taylor, J.; Cristianini, N. Kernel Methods for Pattern Analysis; Cambridge University Press: Cambridge, MA, 2004.
24. Aizerman, M. A.; Braverman, E. M.; Rozonoér, L. I. Theoretical foundations of the potential function method in pattern recognition learning. Autom. Remote Control (Engl. Trans.) 1964, 25, 821-837.
25. Aronszajn, N. Theory of reproducing kernels. Trans. Am. Math. Soc. 1950, 68, 337-404.
26. Güner, O. F. Pharmacophore Perception, Development, and Use in Drug Design; International University Line: La Jolla, CA, 2000; Vol. 2, IUL Biotechnology Series.
27. Haussler, D. Convolution Kernels on Discrete Structures; Technical Report UCSC-CRL-99-10, U. C. Santa Cruz: Santa Cruz, CA, 1999.
28. Gärtner, T.; Flach, P.; Wrobel, S. On graph kernels: Hardness results and efficient alternatives. In Proceedings of the Sixteenth Annual Conference on Computational Learning Theory and the Seventh Annual Workshop on Kernel Machines; Schölkopf, B., Warmuth, M., Eds.; Heidelberg: Heidelberg, Germany, 2003; Vol. 2777, Lecture Notes in Computer Science, pp 129-143.
29. Horváth, T.; Gärtner, T.; Wrobel, S. Cyclic pattern kernels for predictive graph mining. In Proceedings of the Tenth International Conference on Knowledge Discovery and Data Mining; ACM Press: New York, 2004; pp 158-167.
30. Swamidass, S. J.; Chen, J.; Bruand, J.; Phung, P.; Ralaivola, L.; Baldi, P. Kernels for small molecules and the prediction of mutagenicity, toxicity and anti-cancer activity. Bioinformatics 2005, 21 (Suppl. 1), i359-i368.
31. Leslie, C.; Eskin, E.; Noble, W. S. The spectrum kernel: A string kernel for SVM protein classification. In Proceedings of the Pacific Symposium on Biocomputing 2002; Altman, R. B, Dunker, A. K., Hunter, L., Lauerdale, K., Klein, T. E., Eds.; World Scientific: River Edge, NJ, 2002; pp 564-575.
32. Sutherland, J. J.; O'Brien, L. A.; Weaver, D. F. Spline-fitting with a genetic algorithm: A method for developing classification structure - activity relationships. J. Chem. Inf. Comput. Sci. 2003, 43 (6), 1906-1915.
33. Fawcett, T. ROC graphs: Notes and practical considerations for data mining researchers; Technical Report 2003-4; HP Laboratories: Palo Alto, CA, 2003.
34. Demšar. J. Statistical Comparisons of Classifiers over Multiple Data Sets. J. Machine Learning Res. 2006, 7, 1-30.
35. Horvath, D.; Jeandenans, C. Neighborhood behavior of in silico structural spaces with respect to in vitro activity spaces-A novel understanding of the molecular similarity principle in the context of multiple receptor binding profiles. J. Chem. Inf. Comput. Sci. 2003, 43 (2), 680-690.
36. Dietterich, T. G.; Lathrop, R. H.; Lozano-Perez, T. Solving the multiple instance problem with axis-parallel rectangles. Artif. Intell. 1997, 89 (1-2), 31-71.
37. Gärtner, T.; Flach, P. A.; Kowalczyk, A.; Smola, A. J. Multi-instance kernels. In Proceedings of the Nineteenth International Conference on Machine Learning; Sammut, C., Hoffmann, A., Eds.; Morgan Kaufmann: San Francisco, CA, 2002; pp 179-186.
38. Andrews, S.; Hofmann, T.; Tsochantaridis, I. Multiple instance learning with generalized support vector machines. In Proceedings of the Eighteenth National Conference on Artificial Intelligence; American Association for Artificial Intelligence: Edmonton, Alberta, Canada, 2002; pp 943-944.
39. Cuturi, M.; Fukumizu, K.; Vert, J. P. Semigroup kernels on measures. J. Machine Learning Res. 2005, 6, 1169-1198.
40. Wolf, L.; Shashua, A. Learning over sets using kernel principal angles. J. Machine Learning Res. 2003, 4, 913-931.
41. Jebara, T.; Kondor, R.; Howard, A. Probability Product Kernels. J. Machine Learning Res. 2004, 5, 819-844.