Predictive activity profiling of drugs by topological-fragment-spectra- based support vector machines

Journal of Chemical Information and Modeling

Volumn 48, Issue 6, 2008, Pages 1152-1160

  Kawai, Kentaro ^a   Fujishima, Satoshi ^a   Takahashi, Yoshimasa ^a  

^a TOYOHASHI UNIVERSITY OF TECHNOLOGY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); FORECASTING; TOPOLOGY;

ACTIVITY PROFILING; DRUG MOLECULES; MULTI-LABEL CLASSIFICATIONS; PLEIOTROPIC EFFECTS; STRUCTURAL FEATURE; SVM CLASSIFIERS; TOPOLOGICAL FRAGMENT SPECTRA; TRAINING SETS;

SUPPORT VECTOR MACHINES;

DRUG;

ARTICLE; ARTIFICIAL INTELLIGENCE; BIOLOGICAL MODEL; CHEMISTRY; CLASSIFICATION; PHARMACOLOGY;

ARTIFICIAL INTELLIGENCE; MODELS, BIOLOGICAL; PHARMACEUTICAL PREPARATIONS; PHARMACOLOGY;

EID: 47349113899     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci7004753     Document Type: Article

References (38)

1. FDA's Drug Safety Initiative. http://www.fda.gov/cder/drugSafety.htm (accessed Feb 19, 2008).
2. ADMET Descriptors in Discovery Studio; Accelrys: San Diego, CA.
3. DEREK for Windows; Lhasa limited: Leeds, West Yorkshire, U.K.
4. Mohan, C. G.; Gandhi, T.; Garg, D.; Shinde, R. Computer-Assisted Methods in Chemical Toxicity Prediction. Mini Rev. Med. Chem. 2007, 7, 499-507.
5. van de Waterbeemd, H.; Gifford, E. ADMET in Silico Modelling: Towards Prediction Paradise. Nat. Rev. Drug Discovery 2003, 2, 192-204.
6. Lewis, D. F. V. Computer-Assisted methods in the evaluation of chemical toxicity. In Reviews in Computational Chemistry; Lipkowitz, K. B., Boyd, D. B., Eds.; John Wiley & Sons: Hoboken, NJ, 2007; Vol. 3, pp 173-222.
7. Yoshida, K.; Niwa, T. Quantitative Structure-Activity Relationship Studies on Inhibition of HERG Potassium Channels. J. Chem. Inf. Model. 2006, 46, 1371-1378.
8. Snyder, R. D.; Smith, M. D. Computational Prediction of Genotoxicity: Room for Improvement. Drug Discovery Today 2005, 10, 1119-1124.
9. Dearden, J. C. In Silico Prediction of Drug Toxicity. J.Comput.-Aided Mol. Des. 2003, 17, 119-127.
10. Cheng, A.; Dixon, S. L. In Silico Models for the Prediction of Dosedependent Human Hepatotoxicity. J. Comput.-Aided Mol. Des. 2003, 17, 811-823.
11. Ekins, S.; Bravi, G.; Binkley, S.; Gillespie, J. S.; Ring, B. J.; Wikel, J. H.; Wrighton, S. A. Three- and Four-dimensional Quantitative Structure Activity Relationship Analyses of Cytochrome P-450 3A4 Inhibitors. J. Pharmacol. Exp. Ther. 1999, 290, 429-438.
12. Farmer, J. A. Pleiotropic Effects of Statins. Curr. Atheroscler. Rep. 2000, 2, 208-217.
13. Davignon, J. Beneficial Cardiovascular Pleiotropic Effects of Statins. Circulation 2004, 109, 39-43.
14. Xie, L.; Wang, J.; Bourne, P. E. In Silico Elucidation of the Molecular Mechanism Defining the Adverse Effect of Selective Estrogen Receptor Modulators. PLoS Comput. Biol. 2007, 3, e217.
15. Bender, A.; Scheiber, J.; Glick, M.; Davies, J. W.; Azzaouji, K.; Hamon, J.; Urban, L.; Whitebread, S.; Jenkins, J. L. Analysis of Pharmacology Data and the Prediction of Adverse Drug Reactions and Off-Target Effects from Chemical Structure. ChemMedChem 2007, 2, 861-873.
16. Chen, Y. Z.; Zhi, D. G. Ligand-Protein Inverse Docking and Its Potential Use in the Computer Search of Protein Targets of a Small Molecule. Proteins 2001, 43, 217-226.
17. Nettles, J. H.; Jenkins, J. L.; Bender, A.; Deng, Z.; Davies, J. W.; Glick, M. Bridging Chemical and Biological Space: "Target Fishing" Using 2D and 3D Molecular Descriptors. J. Med. Chem. 2006, 49, 6802-6810.
18. Zhou, Y.; Zhou, B.; Chen, K.; Yan, S. F.; King, F. J.; Jiang, S.; Winzeler, E. A. Large-Scale Annotation of Small-Molecule Libraries Using Public Databases. J. Chem. Inf. Model. 2007, 47, 1386-1394.
19. Poroikov, V. V.; Filimonov, D. A.; Ihlenfeldt, W. D.; Gloriozova, T. A.; Lagunin, A. A.; Borodina, Y. V.; Stepanchikova, A. V.; Nicklaus, M. C. PASS Biological Activity Spectrum Predictions in the Enhanced Open NCI Database Browser. J. Chem. Inf. Comput. Sci. 2003, 43, 228-236.
20. Anzail, S.; Barnickel, G.; Cezanne, B.; Krug, M.; Filimonov, D.; Poroikov, V. Discriminating between Drugs and Nondrugs by Prediction of Activity Spectra for Substances (PASS). J. Med. Chem. 2001, 44, 2432-2437.
21. Filimonov, D.; Poroikov, V.; Borodina, Y.; Gloriozova, T. Chemical Similarity Assessment through Multilevel Neighborhoods of Atoms: Definition and Comparison with the Other Descriptors. J. Chem. Inf. Comput. Sci. 1999, 39, 666-670.
22. Poroikov, V. V.; Filimonov, D. A.; Borodina, Y. V.; Lagunin, A. A.; Kos, A. Robustness of Biological Activity Spectra Predicting by Computer Program PASS for Noncongeneric Sets of Chemical Compounds. J. Chem. Inf. Comput. Sci. 2000, 40, 1349-1355.
23. Nidhi; Glick, M.; Davies, J. W.; Jenkins, J. L. Prediction of Biological Targets for Compounds Using Multiple-Category Bayesian Models Trained on Chemogenomics Databases. J. Chem. Inf. Model. 2006, 46, 1124-1133.
24. Lepp, Z.; Kinoshita, T.; Chuman, H. Screening for New Antidepressant Leads of Multiple Activities by Support Vector Machines. J. Chem. Inf. Model. 2006, 46, 158-167.
25. 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, 1882-1889.
26. Fujishima, S.; Takahashi, Y. Classification of Dopamine Antagonists Using TFS-Based Artificial Neural Network. J. Chem. Inf. Comput. Sci. 2004, 44, 1006-1009.
27. Takahashi, Y.; Ohoka, H.; Ishiyama, Y. Structural Similarity Analysis Based on Topological Fragment Spectra. In Advances in Molecular Similarity; Carbo, R., Mezey, P., Eds.; JAI Press: Greenwich, CT, 1998; Vol. 2, pp 93-104.
28. Takahashi, Y.; Nishikoori, K.; Fujishima, S. Classification of Pharmacological Activity of Drugs Using Support Vector Machine. In Active Mining, Second International Workshop, Lecture Notes in Computer Science; Tsumoto, S., Yamaguchi, T., Numao, M., Motoda, H., Eds.; Springer: Berlin, 2005; Vol. 3430, pp 303-311.
29. Takahashi, Y.; Fujishima, S.; Nishikoori, K.; Kato, H.; Okada, T. Identification of Dopamine D1 Receptor Agonists and Antagonists Under Existing Noise Compounds by TFS-based ANN and SVM. J. Comput. Chem. Jpn. 2005, 4, 43-48.
30. Fujishima, S.; Takahashi, Y.; Nishikoori, K.; Kato, H.; Okada, T. Extended Study of the Classification of Dopamine Receptor Agonists and Antagonists using a TFS-based Support Vector Machine. New Gener. Comput. 2007, 25, 203-212.
31. Hristozov, D.; Gasteiger, J.; Costa, F. B. D. Multilabeled Classification Approach To Find a Plant Source for Terpenoids. J. Chem. Inf. Model. 2008, 48, 56-67.
32. Tsoumakas, G.; Katakis, I. Multi-Label Classification: An Overview. Int. J. Data Warehousing Mining 2007, 3, 1-13.
33. MDL Drug Data Report, version 2001.1; MDL Information Systems, Inc.: Santa Clara, CA, 2001.
34. Vapnik, V. N. The Nature of Statistical Learning Theory; Springer: New York, 1995.
35. Piatt, J. C. Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines; Technical Report MSR-TR-98-14, Microsoft Research: Redmond, WA, 1998.
36. Duffy, S. W.; Chen, H. H.; Tabar, L.; Fagerberg, G.; Paci, E. Sojourn Time, Sensitivity and Positive Predictive Value of Mammography Screening for Breast Cancer in Women Aged 40-49. Int. J. Epidemiol. 1996, 25, 1139-1145.
37. Takahashi, Y. Chemical data mining based on non-terminal vertex graph. In Systems, Man and Cybernetics, 2004 IEEE International Conference; IEEE: Piscataway, NJ, 2004; Vol. 5, pp 4583-4587.
38. Bemis, G. W.; Murcko, M. A. The Properties of Known Drugs. 1. Molecular Frameworks. J. Med. Chem. 1996, 39, 2887-2893.