Determining the validity of a QSAR model - A classification approach

Journal of Chemical Information and Modeling

Volumn 45, Issue 1, 2005, Pages 65-73

  Guha, Rajarshi ^a   Jurs, Peter C ^a  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; BOILING LIQUIDS; DATA REDUCTION; LINEAR EQUATIONS; MATHEMATICAL MODELS; PROBABILITY; PROBLEM SOLVING; REGRESSION ANALYSIS;

CLASSIFICATION ALGORITHM; QUANTITATIVE STRUCTURE PROPERTY RELATIONSHIPS (QSPR); QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP (QSAR) MODELING; THREE-DIMENSIONAL STRUCTURE;

MOLECULAR STRUCTURE;

ALGORITHM; ARTICLE; CHEMICAL MODEL; CLASSIFICATION; COMPUTER SIMULATION; QUANTITATIVE STRUCTURE ACTIVITY RELATION; REGRESSION ANALYSIS; REPRODUCIBILITY; STATISTICAL MODEL; VALIDATION STUDY;

ALGORITHMS; CLASSIFICATION; COMPUTER SIMULATION; LINEAR MODELS; MODELS, CHEMICAL; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; REGRESSION ANALYSIS; REPRODUCIBILITY OF RESULTS;

EID: 13844321935     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci0497511     Document Type: Article

References (32)

1. Goll, E. S.; Jurs, P. C. Prediction of the normal boiling points of organic compounds from molecular structures with a computational neural network model. J. Chem. Inf. Comput. Sci. 1999, 39, 974-983.
2. Avery, M. A.; Alvim-Gaston, M.; Rodrigues, C. R.; Barreiro, E. J.; Cohen, F. E.; Sabnis, Y. A.; Woolfrey, J. R. Structure activity relationships of the antimalarial agent artemisinin. the development of predictive in vitro potency models using comfa and hqsar methodologies. J. Med. Chem. 2002, 45, 292-303.
3. Guha, R.; Jurs, P. C. The development of QSAR models to predict and interpret the biological activity of artemisinin analogues. J. Chem. Inf. Comput. Sci. 2004, 44, 1440-1449.
4. Liu, S.; Cao, C.; Li, Z. Approach to estimation and prediction for normal boiling point (nbp) of alkanes based on a novel molecular distance edge (mde) vector, lambda. J. Chem. Inf. Comput. Sci. 1998, 38, 387-394.
5. Jurs, P. C.; Chou, J. T.; Yuan, M. Studies of chemical structure biological activity relations using patter recognition. In Computer assisted drug design; Olsen, E. C., Christoffersen, R. E., Eds.; American Chemical Society: Washington, DC, 1979.
6. Stuper, A. J.; Brugger, W. E.; Jurs, P. C. Computer assisted studies of chemical structure and biological function; Wiley: New York, 1979.
7. Goldstein, H. Classical mechanics; Addison-Wesley: Reading, MA, 1950.
8. Pearlman, R. S. Physical chemical properties of drugs; Yalkowsky, S. L., Sinkula, A. A., Valvani, S. C., Eds.; Marcel Dekker, Inc.: New York, 1980.
9. Kier, L. B.; Hall, L. H.; Murray, W. J. Molecular connectivity i: relationship to local anesthesia. J. Pharm. Sci. 1975, 64.
10. Kier, L. B.; Hall, L. H. Molecular connectivity in structure activity analysis; John Wiley & Sons: 1986.
11. Kier, L. B.; Hall, L. H. Molecular connectivity vii: specific treatment to heteroatoms. J. Pharm. Sci. 1976, 65, 1806-1809.
12. Kier, L. B.; Hall, L. H. Molecular structure description, the electrotopological state; Academic Press: London, 1999.
13. Kier, L. B.; Hall, L. H. An electrotopological-state index for atoms in molecules. Pharm. Res. 1990, 7, 801-807.
14. Stanton, D. T.; Jurs, P. C. Development and use of charged partial surface area structural descriptors in computer assissted quantitative structure property relationship studies. Anal. Chem. 1990, 62, 2323-2329.
15. Stanton, D. T.; Mattioni, B. E.; Knittel, J. J.; Jurs, P. C. development and use of hydrophobic surface area (hsa) descriptors for computer-assisted quantitative structure-activity and structure-property relationship studies. J. Chem. Inf. Comput. Sci. 2004, 44, 1010-1023.
16. Mattioni, B. E. The development of qsar models for physical property and biological activity prediction of organic compounds; 2003.
17. Metropolis, N.; Rosenbluth, A. W.; Rosenbluth, M.; Teller, A. H.; Teller, E. Equation of state calculations by fast computing machines. J. Chem. Phys. 1953, 21, 1087-1092.
18. Sutter, J. M.; Dixon, S. L.; Jurs, P. C. Automated descriptor selection for quantitative structure-activity relationships using generalized simulated annealing. J. Chem. Inf. Comput. Sci. 1995, 35, 77-84.
19. Japkowicz, N. Learning from imbalanced data sets: a comparison of various strategies. In Learning from imbalanced data sets: papers from the aaai workshop; AAAI Press: 2000.
20. Kubat, M.; Matwin, S. Addressing the curse of imbalanced training sets: one sided selection. In Proceedings of the fourteenth international conference on machine learning; Morgan Kauffmnan: 1997.
21. Maloof, M. A. Learning when data sets are imbalanced and when costs are unequal and unknown. In ICML-2003 Workshop on Learning from Imbalanced Data Sets II; 2003.
22. Breiman, L. Using convex pseudo data to increase prediction accuracy; 1998.
23. Breiman, L.; Friedman, J. H.; Olshen, R. A.; Stone, C. J. Classification and regresion trees; Bickel, P., Cleveland, W. S., Dudley, R. M., Eds.; Wadsworth International Group: 1984.
24. Svetnik, V.; Liaw, A.; Tong, C.; Culberson, 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, 42, 1947-1958.
25. R Development Core Team. R: a language and environment for statistical computing. 2004, http://www. R-project.org.
26. Carhart, R. E.; Smith, D. E.; Venkataraghavan, R. Atom pairs as molecular features in structure - activity studies: definition and applications. J. Chem. Inf. Comput. Sci. 1985, 25, 64-73.
27. Ripley, B. D. Pattern Recognition and Neural Networks; Cambridge University Press: Oxford, 1996.
28. Weston, J.; Pérez-Cruz, F.; Bousquet, O.; Chapelle, O.; Elisseeff, A.; Schölkopf Feature selection and transaction for prediction of molecular bioactivity for drug design. Bioinformatics 2003, 19, 764-771.
29. Randic, M. On molecular idenitification numbers. J. Chem. Inf. Comput. Sci. 1984, 24, 164-175.
30. Sharma, V.; Goswami, A.; Madan, A. K. Eccentric connectivity index: a novel highly discriminating topological descriptor for structure - property and structure - activity studies. J. Chem. Inf. Comput. Sci. 1998, 37, 273-282.
31. Stouch, T. R.; Jurs, P. C. A simple method for the representation, quantification and comparison of the volumes and shapes of chemical compounds. J. Chem. Inf. Comput. Sci. 1986, 26, 4-12.
32. Rohrbaugh, R. H.; Jurs, P. C. Molecular shape and prediction of high performace liquid Chromatographic retention indices of polycyclic aromatic hydrocarbons. Anal. Chem. 1987, 59, 1048.