Ligand-target prediction using winnow and naive bayesian algorithms and the implications of overall performance statistics

Journal of Chemical Information and Modeling

Volumn 48, Issue 12, 2008, Pages 2313-2325

  Nigsch, Florian ^a   Bender, Andreas ^b,c   Jenkins, Jeremy L ^b   Mitchell, John B O ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^b NOVARTIS INSTITUTES FOR BIOMEDICAL RESEARCH   (United States)

^c LEIDEN UNIVERSITY   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

LIGANDS;

BAYESIAN CLASSIFIER; CROSS VALIDATION; NAIVE BAYESIAN ALGORITHMS; OVERALL ACCURACIES; PERFORMANCE ASSESSMENT; PERFORMANCE STATISTICS; TARGET PREDICTION; WINNOW ALGORITHM;

FORECASTING;

CYCLIN B; CYCLIN DEPENDENT KINASE 1; LIGAND; PHOSPHODIESTERASE V;

ALGORITHM; ARTICLE; BAYES THEOREM; CLASSIFICATION; COMPARATIVE STUDY; COMPUTER INTERFACE; DRUG DESIGN; DRUG DEVELOPMENT; DRUG SCREENING; FACTUAL DATABASE; METABOLISM; METHODOLOGY; MONTE CARLO METHOD; STATISTICS;

ALGORITHMS; BAYES THEOREM; CDC2 PROTEIN KINASE; CYCLIC NUCLEOTIDE PHOSPHODIESTERASES, TYPE 5; CYCLIN B; DATABASES, FACTUAL; DRUG DESIGN; DRUG DISCOVERY; DRUG EVALUATION, PRECLINICAL; LIGANDS; MONTE CARLO METHOD; USER-COMPUTER INTERFACE;

EID: 58149116805     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci800079x     Document Type: Article

References (36)

1. Hert, J.; Willett, P.; Wilton, D. J.; Acklin, P.; Azzaoui, K.; Jacoby, E.; Schuffenhauer, A. New methods for ligand-based virtual screening: use of data fusion and machine learning to enhance the effectiveness of similarity searching. J. Chem. Inf. Model. 2006, 46, 462-470.
2. Keiser, M. J.; Roth, B. L.; Armbruster, B. N.; Ernsberger, P.; Irwin, J. J.; Shoichet, B. K. Relating protein pharmacology by ligand chemistry. Nat. Biotechnol. 2007, 25, 197-206.
3. Sheridan, R. P.; Kearsley, S. K. Why do we need so many chemical similarity search methods? Drug Discovery Today 2002, 7, 903-911.
4. Bender, A.; Glen, R. C. Molecular similarity: a key technique in molecular informatics. Org. Biomol. Chem. 2004, 2, 3204-3218.
5. Senese, C. L.; Duca, J.; Pan, D.; Hopfinger, A. J.; Tseng, Y. J. 4D-fingerprints, universal QSAR and QSPR descriptors. J. Chem. Inf. Comput. Sci. 2004, 44, 1526-1539.
6. Hughes, L. D.; Palmer, D. S.; Nigsch, F.; Mitchell, J. B. O. Why Are Some Properties More Difficult To Predict than Others? A Study of QSPR Models of Solubility, Melting Point, and Log P. J. Chem. Inf. Model 2008, 48, 220-232.
7. MDL MACCS Keys; Elsevier MDL: San Ramon, CA, 2008.
8. Sybyl, version 7; Tripos: St. Louis, MO, 2007.
9. Bender, A.; Mussa, H. Y.; Glen, R. C.; Reiling, S. Molecular similarity searching using atom environments, information-based feature selection, and a naive Bayesian classifier. J. Chem. Inf. Comput. Sci. 2004, 44, 170-178.
10. Glen, R. C.; Bender, A.; Arnby, C. H.; Carlsson, L.; Boyer, S.; Smith, J. Circular fingerprints: flexible molecular descriptors with applications from physical chemistry to ADME. IDrugs 2006, 9, 199-204.
11. Rogers, D.; Brown, R. D.; Hahn, M. Using extended-connectivity fingerprints with Laplacian-modified Bayesian analysis in high-throughput screening follow-up. J. Biomol. Screen. 2005, 10, 682-686.
12. Arodz, T.; Yuen, D. A.; Dudek, A. Z. Ensemble of linear models for predicting drug properties. J. Chem. Inf. Model. 2006, 46, 416-423.
13. Cannon, E. O.; Arnim, A.; Bender, A.; Sternberg, M. J. E.; Muggleton, S. H.; Glen, R. C.; Mitchell, J. B. O. Support vector inductive logic programming outperforms the naive Bayes classifier and inductive logic programming for the classification of bioactive chemical compounds. J. Comput.-Aided. Mol. Des. 2007, 21, 269-280.
14. Breiman, L. Random Forests. Machine Learning 2001, 45, 5-32.
15. Wilton, D. J.; Harrison, R. F.; Willett, P.; Delaney, J.; Lawson, K.; Mullier, G. Virtual, screening using binary kernel, discrimination: analysis of pesticide data. J. Chem. Inf. Model 2006, 46, 471-477.
16. Mahé, P.; Ralaivola, L.; Stoven, V.; Vert, J. P. The pharmacophore kernel for virtual, screening with support vector machines. J. Chem. Inf. Model. 2006, 46, 2003-2014.
17. Molnar, L.; Keseru, G. M. A neural network based virtual screening of cytochrome P450 3A4 inhibitors. Bioorg. Med. Chem. Lett. 2002, 12, 419-421.
18. Breiman, L.; Friedman, J.; Stone, C. J.; Olshen, R. In Classification and Regression Trees, 1st Edition; CRC Press LLC: Boca Raton, FL, 1984.
19. Jorissen, R. N.; Gilson, M. K. Virtual screening of molecular databases using a support vector machine. J. Chem. Inf. Model. 2005, 45, 549-561.
20. Agrafiotis, D. K.; Cedeno, W. C.; Lobanov, V. S. On the use of neural network ensembles in QSAR and QSPR. J. Chem. Inf. Comput. Sci. 2002, 42, 903-911.
21. Lagunin, A.; Stepanchikova, A.; Filimonov, D.; Poroikov, v. PASS: prediction of activity spectra for biologically active substances. Bioinformatics 2000, 16, 747-748.
22. Poroikov, V.; Filimonov, D.; Lagunin, A.; Gloriozova, T.; Zakharov, A. PASS: identification of probable targets and mechanisms of toxicity. SAR QSAR Environ. Res. 2007, 18, 101-110.
23. Fliri, A. F.; Loging, W. T.; Thadeio, P. F.; Volkmann, R. A. Biological spectra analysis: Linking biological activity profiles to molecular structure. Proc. Natl. Acad. Sci., U.S.A. 2005, 102, 261-266.
24. Bender, A.; Jenkins, J. L.; Glick, M.; Deng, Z.; Nettles, J. H.; Davies, J. W. "Bayes affinity fingerprints" improve retrieval rates in virtual screening and define orthogonal bioactivity space: when are multitarget drugs a feasible concept. J. Chem. Inf. Model. 2006, 46, 2445-2456.
25. 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.
26. Olah, M.; Mracec, M.; Ostopovici, L.; Rad, R.; Bora, A.; Hadaruga, N.; Olah, I.; Banda, M.; Simon, Z.; Oprea, T. I. WOMBAT: World of Molecular Bioactivity. In Cheminformatics in Drug Discovery; Oprea, T. I., Ed.; Wiley-VCH: New York, 2004; pp 223-239.
27. Young, D. W.; Bender, A.; Hoyt, J.; McWhinnie, E.; Chirn, G.; Tao, C. Y.; Tallarico, J. A.; Labow, M.; Jenkins, J. L.; Mitchison, T. J.; Feng, Y. Integrating high-content screening and ligand-target prediction to identify mechanism of action. Nat. Chem. Biol. 2008, 4, 59-68.
28. Jenkins, J. L.; Bender, A.; Davies, J. W. In silico target fishing: Predicting biological targets from chemical structure. Drug Discovery Today: Technol. 2006, 3, 413-421.
29. Siefkes, C.; Assis, F.; Chhabra, S.; Yerazunis, W. S. Combining Winnow and Orthogonal Sparse Bigrams for Incremental Spam Filtering. In Proceedings of the European Conference on Principle and Practice of Knowledge Discovery in Databases, 2004; Springer: Berlin, 2004; pp 410-421.
30. Nigsch, F.; Mitchell, J. B. O. How To Winnow Actives from Inactives: Introducing Molecular Orthogonal Sparse Bigrams (MOSBs) and Multiclass Winnow. J. Chem. Inf. Model. 2008, 48, 306-318.
31. Littlestone, N. Learning Quickly When Irrelevant Attributes Abound: A New Linear-threshold Algorithm. Machine Learning 1988, 2, 285-318.
32. Dagan, I.; Karov, Y.; Roth, D. Mistake-driven learning in textcategorization. In Proceedings of the 2nd Conference on Empirical Methods in Natural Language Processing; Association for Computational Linguistics (ACL): Somerset, NJ, 1997; pp 55-63.
33. Stirzaker, D. In Probability and Random Variables; Cambridge University Press: Cambridge, U.K., 1999.
34. Lipinski, C. A.; Lombardo, 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, 3-26.
35. R Development Core Team R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2008.
36. Kubinyi, H. Molecular similarity. 2. The structural basis of drug design. Pharm. Unserer Zeit 1998, 27, 158-172.