Decision tree methods in pharmaceutical research

Current Topics in Medicinal Chemistry

Volumn 6, Issue 1, 2006, Pages 31-39

  Blower, Paul E ^a   Cross, Kevin P ^a  

^a Leadscope Inc   (United States)

Author keywords

Combinatorial chemistry; Decision Forest (DF); Molconn X topological descriptors; Pharmacophores; Recursive partitioning; SAR; Sequential screening

Indexed keywords

4 AMINOBUTYRIC ACID RECEPTOR; BENZODIAZEPINE RECEPTOR; CITRININ; CYCLOOXYGENASE 1 INHIBITOR; CYCLOOXYGENASE 2 INHIBITOR; CYTOCHROME P450 INHIBITOR; DIHYDROFOLATE REDUCTASE INHIBITOR; DRUG; ESTROGEN RECEPTOR; G PROTEIN COUPLED RECEPTOR; LIGAND; MONOAMINE OXIDASE INHIBITOR; PYRIMIDINE DERIVATIVE;

ACCURACY; ALGORITHM; COMBINATORIAL CHEMISTRY; DECISION THEORY; DISCRIMINANT ANALYSIS; DRUG INDUSTRY; DRUG RESEARCH; DRUG SCREENING; HIGH THROUGHPUT SCREENING; HUMAN; INTERMETHOD COMPARISON; PHARMACOPHORE; PREDICTION; QUANTITATIVE STRUCTURE ACTIVITY RELATION; REVIEW; SENSITIVITY AND SPECIFICITY; STATISTICAL ANALYSIS; CHEMISTRY; DECISION TREE; DRUG DESIGN; METHODOLOGY;

CHEMISTRY, PHARMACEUTICAL; DECISION TREES; DRUG DESIGN; HUMANS; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 33644855553     PISSN: 15680266     EISSN: None     Source Type: Journal    
DOI: 10.2174/156802606775193301     Document Type: Review

References (43)

1. Breiman, L.; Friedman, J. H.; Olshen, R. A.; Stone, C. J. Classification and Regression Trees; Wadsworth, 1984.
2. Zupan, J.; Gasteiger, J. Neural Networks for Chemists; Wiley-VCH: Weinheim, 1999.
3. Hastie, T.; Tibshirani, R.; Friedman, J. The Elements of Statistical Learning; Springer-Verlag: New York, 2001.
4. Zernov, V. V.; Balakin, K. V.; Ivaschenko, A. A.; Savchuk, N. P.; Pletnev, I. V. Drug discovery using support vector machines. The case studies of drug-likeness, agrochemical-likeness, and enzyme inhibition predictions. J. Chem. Inf. Comput. Sci. 2003, 43, 2048-2056.
5. Harper, G.; Bradshaw, J.; Gittins, J. C.; Green, D. V.; Leach, A. R. Prediction of biological activity for high-throughput screening using binary kernel discrimination. J. Chem. Inf. Comput. Sci. 2001, 41, 1295-1300.
6. Young, S. S.; Hawkins, D. M. Analysis of a 2(9) full factorial chemical library. J. Med. Chem. 1995, 38, 2784-2788.
7. Hawkins, D. M.; Young, S. S.; Rusinko, A., 3rd Analysis of a large structure-activity data set using recursive partitioning. Quant. Struct-Act Relat. 1997, 16, 1-7.
8. Young, S. S.; Hawkins, D. M. Using recursive partitioning to analyze a large SAR data set. SAR QSAR Env. Res. 1998, 8, 183-193.
9. Chen, X.; Rusinko, A., 3rd; Young, S. S. Recursive Partitioning Analysis of a Large Structure-Activity Data Set Using Three-Dimensional Descriptors. J. Chem. Inf. Comput. Sci. 1998, 38, 1054-1062.
10. Rusinko, A., 3rd; Farmen, M. W.; Lambert, C. G.; Brown, P. L.; Young, S. S. Analysis of a large structure/biological activity data set using recursive partitioning. J. Chem. Inf. Comput. Sci. 1999, 39, 1017-1026.
11. Hawkins, D. M.; Basak, S. C.; Mills, D. Assessing model fit by cross-validation. J. Chem. Inf. Comput. Sci. 2003, 43, 579-586.
12. Nonconfidential screening results and chemical structural data from the NCI's Developmental Therapeutics Program are available online: Human Tumor Cell Line Screen: http.//dtp.nci.nih.gov/docs/cancer/cancer_data.html
13. Roberts, G.; Myatt, G. J.; Johnson, W. P.; Cross, K. P.; Blower, P. E., Jr. LeadScope: software for exploring large sets of screening data. J. Chem. Inf. Comput. Sci. 2000, 40, 1302-1314.
14. Chen, X.; Rusinko, A., 3rd; Tropsha, A.; Young, S. S. Automated pharmacophore identification for large chemical data sets. J. Chem. Inf. Comput. Sci. 1999, 39, 887-896.
15. Cho, S. J.; Shen, C. F.; Hermsmeier, M. A. Binary formal inference-based recursive modeling using multiple atom and physicochemical property class pair and torsion descriptors as decision criteria. J. Chem. Inf. Comput. Sci. 2000, 40, 668-680.
16. 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, 1906-1915.
17. Sutherland, J. J.; Weaver, D. F. Development of quantitative structure-activity relationships and classification models for anticonvulsant activity of hydantoin analogues. J. Chem. Inf. Comput. Sci. 2003, 43, 1028-1036.
18. Dunn III, W. J.; Wold, S. SIMCA Pattern Recognition and Classification. Chemometric Methods in Molecular Design; VCH: New York, 1995; pp 179-193.
19. van Rhee, A. M.; Stocker, J.; Printzenhoff, D.; Creech, C.; Wagoner, P. K.; Spear, K. L. Retrospective analysis of an experimental high-throughput screening data set by recursive partitioning. J. Comb. Chem. 2001, 3, 267-277.
20. Higgs, R. E.; Bemis, K. G.; Watson, I. A.; Wikel, J. H. Experimental Designs for Selecting Molecules from Large Chemical Databases. J. Chem. Inf. Comput. Sci. 1997, 37, 861-870.
21. Jones-Hertzog, D. K.; Mukhopadhyay, P.; Keefer, C. E.; Young, S. S. Use of recursive partitioning in the sequential screening of G-protein-coupled receptors. J. Pharmacol. Toxicol. Methods 1999, 42, 207-215.
22. Rusinko, A., 3rd; Young, S. S.; Drewry, D. H.; Gerritz, S. W. Optimization of focused chemical libraries using recursive partitioning. Comb. Chem. High Throughput Screen 2002, 5, 125-133.
23. Bai, J. P.; Utis, A.; Crippen, G.; He, H. D.; Fischer, V.; Tullman, R.; Yin, H. Q.; Hsu, C. P.; Jiang, L.; Hwang, K. K. Use of classification regression tree in predicting oral absorption in humans. J. Chem. Inf. Comput. Sci. 2004, 44, 2061-2069.
24. Feng, J.; Lurati, L.; Ouyang, H.; Robinson, T.; Wang, Y.; Yuan, S.; Young, S. S. Predictive Toxicology: Benchmarking Molecular Descriptors and Statistical Methods. J. Chem. Inf. Comput. Sci. 2003, 43, 1463-1470.
25. Izrailev, S.; Agrafiotis, D. A novel method for building regression tree models for QSAR based on artificial ant colony systems. J. Chem. Inf. Comput. Sci. 2001, 41, 176-180.
26. Miller, D. W. Results of a new classification algorithm combining K nearest neighbors and recursive partitioning. J. Chem. Inf. Comput. Sci. 2001, 41, 168-175.
27. Friedman, J. H. Flexible Metric Nearest Neighbor Classification; Department of Statistics, Stanford University: Stanford, CA, 1994.
28. Blower, P.; Fligner, M.; Verducci, J.; Bjoraker, J. On combining recursive partitioning and simulated annealing to detect groups of biologically active compounds. J. Chem. Inf. Comput. Sci. 2002, 42, 393-404.
29. Cross, K. P.; Blower, P. E.; Myatt, G. J. Identifying Discriminating Structural Features for Compound Classes. In 223rd ACS National Meeting: Orlando, FL, 2002.
30. DeLisle, R. K.; Dixon, S. L. Induction of decision trees via evolutionary programming. J. Chem. Inf. Comput. Sci. 2004, 44, 862-870.
31. Godden, J. W.; Furr, J. R.; Bajorath, J. Recursive median partitioning for virtual screening of large databases. J. Chem. Inf. Comput. Sci. 2003, 43, 182-188.
32. Miller, D. W. A chemical class-based approach to predictive model generation. J. Chem. Inf. Comput. Sci. 2003, 43, 568-578.
33. Dixon, S. L.; Villar, H. O. Investigation of classification methods for the prediction of activity in diverse chemical libraries. J. Comput. Aided Mol. Des. 1999, 13, 533-545.
34. Svetnik, V.; Liaw, A.; Tong, C.; Culberson, J. C.; Sheridan, R. P.; Feuston, B. P. Random forest: a classification and regression tool for compound classification and QSAR modeling. J. Chem. Inf. Comput. Sci. 2003, 43, 1947-1958.
35. Efton, B.; Tibshirani, R. An Introduction to the Bootstrap; Chapman and Hall: New York, 1993.
36. Tong, W.; Hong, H.; Fang, H.; Xie, Q.; Perkins, R. Decision forest: combining the predictions of multiple independent decision tree models. J. Chem. Inf. Comput. Sci. 2003, 43, 525-531.
37. Sussman, N. B.; Arena, V. C.; Yu, S.; Mazumdar, S.; Thampatty, B. P. Decision tree SAR models for developmental toxicity based on an FDA/ TERIS database. SAR QSAR Environ Res. 2003, 14, 83-96.
38. Breiman, L. Bagging Predictors. Machine Learning 1996, 24, 123-140.
39. Susnow, R. G.; Dixon, S. L. Use of robust classification techniques for the prediction of human cytochrome P450 2D6 inhibition. J. Chem. Inf. Comput. Sci. 2003, 43, 1308-1315.
40. Cheng, A.; Dixon, S. L. In silico models for the prediction of dose-dependent human hepatotoxicity. J. Comput. Aided Mol. Des. 2003, 17, 811-823.
41. van Rhee, A. M. Use of recursion forests in the sequential screening process: consensus selection by multiple recursion trees. J. Chem. Inf. Comput. Sci. 2003, 43, 941-948.
42. Stockfisch, T. P. Partially Unified Multiple Property Recursive Partitioning (PUMP-RP): A New Method for Predicting and Understanding Drug Selectivity. J. Chem. Inf. Comput. Sci. 2003, 43, 1608-1613.
43. Rao, S. N.; Stockfisch, T. P. Partially Unified Multiple Property Recursive Partitioning (PUMP-RP) Analyses of Cyclooxygenase (COX) Inhibitors. J. Chem. Inf. Comput. Sci. 2003, 43, 1614-1622.