Predictive quantitative structure-activity relationships modeling: Development and validation of QSAR models

Handbook of Chemoinformatics Algorithms

Volumn , Issue , 2010, Pages 211-232

  Tropsha, Alexander ^a   Golbraikh, Alexander ^a  

^a UNIVERSITY OF NORTH CAROLINA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85046423034     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1201/9781420082999     Document Type: Chapter

References (42)

1. Kovatcheva, A., Golbraikh, A., Oloff, S., Xiao, Y. D., Zheng, W., Wolschann, P., Buchbauer, G., and Tropsha, A., Combinatorial QSAR of ambergris fragrance compounds. J. Chem. Inf. Comput. Sci. 2004, 44, 582-595.
2. Kovatcheva, A., Golbraikh, A., Oloff, S., Feng, J., Zheng, W., and Tropsha, A., QSAR modeling of datasets with enantioselective compounds using chirality sensitive molecular descriptors. SAR QSAR Environ. Res. 2005, 16, 93-102.
3. de Cerqueira, L. P., Golbraikh, A., Oloff, S., Xiao, Y., and Tropsha, A., Combinatorial QSAR modeling of P-glycoprotein substrates. J. Chem. Inf. Model. 2006, 46, 1245-1254.
4. Wang, X. S., Tang, H., Golbraikh, A., and Tropsha, A., Combinatorial QSAR modeling of specificity and subtype selectivity of ligands binding to serotonin receptors 5HT1E and 5HT1F. J. Chem. Inf. Model. 2008, 48, 997-1013.
5. Golbraikh, A. and Tropsha, A., Beware of Q2! J. Mol. Graph. Model. 2002, 20, 269-276.
6. Saliner, A. G., Netzeva, T. I., and Worth, A.P., Prediction of estrogenicity: Validation of a classification model. SAR QSAR. Environ. Res. 2006, 17, 195-223.
7. Hsieh, J. H., Wang, X. S., Teotico, D., Golbraikh, A., and Tropsha, A., Differentiation of AmpC beta-lactamase binders vs. decoys using classification KNN QSAR modeling and application of the QSAR classifier to virtual screening. J. Comput. Aided Mol. Des. 2008, 22, 593-609.
8. Li, Y., Pan, D., Liu, J., Kern, P. S., Gerberick, G. F., Hopfinger, A. J., and Tseng, Y. J., Categorical QSAR models for skin sensitization based upon local lymph node assay classification measures part 2: 4D-Fingerprint three-state and two-2-state logistic regression models. Toxicol. Sci. 2007, 99, 532-544.
9. Efron, B., Bootstrap methods: Another look at the jackknife. The Annals of Statistics 1979, 7, 1-26.
10. Mooney, C. Z. and Duval, R. D., Bootstrapping. A Non-Parametric Approach to Statistical Inference. Sage, Newbury Park, CA, 1993.
11. DiCiccio, T. J. and Efron, B., Bootstrap confidence intervals. Statist. Sci. 1996, 11, 189-228.
12. Efron, B., Better bootstrap confidence intervals. J. Amer. Statist. Assoc. 1987, 82, 171-220.
13. Isaksson, A., Wallman, M., Göransson, H., and Gustafsson, M. G., Cross-validation and bootstrapping are unreliable in small sample classification. Pattern Recogn. Lett. 2008, 29, 1960-1965.
14. Wickenberg-Bolin, U., Goransson, H., Fryknas, M., Gustafsson, M. G., and Isaksson, A., Improved variance estimation of classification performance via reduction of bias caused by small sample size. BMC Bioinform. 2006, 7, 127.
15. Jaworska, J., Nikolova-Jeliazkova, N., and Aldenberg, T., QSAR applicabilty domain estimation by projection of the training set descriptor space: A review. Altern. Lab Anim. 2005, 33, 445-459.
16. Jaworska, J. and Nikolova-Jeliazkova, N., Reviewof methods to assess aQSARapplicability domain, http://ambit.acad.bg/nina/publications/2004/AppDomain_qsar04.ppt, 2008.
17. Nikolova-Jeliazkova, N. and Jaworska, J., An approach to determining applicability domains for QSAR group contribution models: An analysis of SRC KOWWIN. Altern. Lab Anim. 2005, 33, 461-470.
18. Netzeva, T. I., Gallegos, S. A., and Worth, A. P., Comparison of the applicability domain of a quantitative structure-activity relationship for estrogenicity with a large chemical inventory. Environ. Toxicol. Chem. 2006, 25, 1223-1230.
19. Fechner, N., Hinselmann, G., Schmiedl, C., and Zell, A., Estimating the applicability domain of Kernel-based QSPR models using classical descriptor vectors. pdf. Chem. Central J. 2008, 2(Suppl. 1), 2.
20. Afantitis, A., Melagraki, G., Sarimveis, H., Koutentis, P. A., Markopoulos, J., and Igglessi-Markopoulou, O., A Novel QSAR model for predicting induction of apoptosis by 4-Aryl-4H-chromenes. Bioorg. Med. Chem. 2006, 14, 6686-6694.
21. Govindraju, V., Pattern Recognition Review (1)-Definitions, Bayesian Classifiers, http:/ /www.cedar.buffalo.edu/~govind/CSE666/fall2007/Pattern_recognition_lecture_ slides_1.pdf, 2008.
22. Guha, R., Dutta, D., Jurs, P. C., and Chen, T., R-NN curves:An intuitive approach to outlier detection using a distance based method. J. Chem. Inf. Model. 2006, 46, 1713-1722.
23. Kuhne, R., Ebert, R. U., and Schuurmann, G., Model selection based on structural similarity-method description and application to water solubility prediction. J. Chem. Inf. Model. 2006, 46, 636-641.
24. Zhang, L., Zhu, H., Oprea, T. I., Golbraikh, A., and Tropsha, A., QSAR modeling of the blood-brain barrier permeability for diverse organic compounds. Pharm. Res. 2008, 25, 1902-1914.
25. Tetko, I. V., Bruneau, P., Mewes, H. W., Rohrer, D. C., and Poda, G. I., Can we estimate the accuracy of ADME-tox predictions? Drug Discov. Today 2006, 11, 700-707.
26. Netzeva, T. I., Worth, A., Aldenberg, T., Benigni, R., Cronin, M. T., Gramatica, P., Jaworska, J. S., et al., Current status of methods for defining the applicability domain of (Quantitative) structure-activity relationships. The Report and Recommendations of ECVAMWorkshop 52. Altern. Lab Anim. 2005, 33, 155-173.
27. Manallack, D. T., Tehan, B. G., Gancia, E., Hudson, B. D., Ford, M. G., Livingstone, D. J., Whitley, D. C., and Pitt, W. R., A consensus neural network-based technique for discriminating soluble and poorly soluble compounds. J. Chem. Inf. Comput. Sci. 2003, 43, 674-679.
28. Bruneau, P. and McElroy, N. R., LogD7.4 Modeling using Bayesian regularized neural networks. Assessment and correction of the errors of prediction. J. Chem. Inf. Model. 2006, 46, 1379-1387.
29. Bruneau, P., Search for predictive generic model of aqueous solubility using Bayesian neural nets. J. Chem. Inf. Comput. Sci. 2001, 41, 1605-1616.
30. Tetko, I.V., Sushko, I., Pandey, A. K., Zhu, H., Tropsha, A., Papa, E., Oberg, T., Todeschini, R., Fourches, D., and Varnek, A., Critical assessment of QSAR models of environmental toxicity against tetrahymena pyriformis: Focusing on applicability domain and overfitting by variable selection 1. J. Chem. Inf. Model. 2008, 48, 1733-1746.
31. Sachs, L., Applied Statistics: A Handbook of Techniques. Springer, NewYork, 1984.
32. Shen, M., Beguin, C., Golbraikh, A., Stables, J. P., Kohn, H., and Tropsha, A., Application of predictive QSAR models to database mining: Identification and experimental validation of novel anticonvulsant compounds. J. Med. Chem. 2004, 47, 2356-2364.
33. Votano, J. R., Parham, M., Hall, L. H., Kier, L. B., Oloff, S., Tropsha, A., Xie, Q., and Tong, W., Three new consensus QSAR models for the prediction of Ames genotoxicity. Mutagenesis 2004, 19, 365-377.
34. Zhang, S., Wei, L., Bastow, K., Zheng, W., Brossi, A., Lee, K. H., and Tropsha, A., Antitumor agents 252. Application of validated QSAR models to database mining: Discovery of novel tylophorine derivatives as potential anticancer agents. J. Comput. Aided Mol. Des. 2007, 21, 97-112.
35. Zhu, H., Tropsha, A., Fourches, D., Varnek, A., Papa, E., Gramatica, P., Oberg, T., Dao, P., Cherkasov, A., and Tetko, I. V., Combinatorial QSAR modeling of chemical toxicants tested against tetrahymena pyriformis. J. Chem. Inf. Model. 2008, 48, 766-784.
36. Vasanthanathan, P., Lakshmi, M., Arockia, B. M., Gupta, A. K., and Kaskhedikar, S. G., QSAR study of 3-phenyl-5-acyloxymethyl-2H,5H-furan-2-ones as antifungal agents: The dominant role of electronic parameter. Chem. Pharm. Bull. (Tokyo) 2006, 54, 583-587.
37. Jorgensen, W. L. and Tirado-Rives, J., QSAR/QSPR and proprietary data. J. Chem. Inf. Model. 2006, 46, 937.
38. Maggiora, G. M., On outliers and activity cliffs-why QSAR often disappoints. J. Chem. Inf. Model. 2006, 46, 1535.
39. Stouch, T. R., Kenyon, J. R., Johnson, S. R., Chen, X. Q., Doweyko, A., and Li, Y., In silico ADME/Tox: Why models fail. J. Comput. Aided Mol. Des. 2003, 17, 83-92.
40. Oprea, T. and Tropsha, A., Target, chemical and bioactivity databases-integration is key. Drug Discov. Today 2006, 3, 357-365.
41. Austin, C. P., Brady, L. S., Insel, T. R., and Collins, F. S., NIH molecular libraries initiative. Science 2004, 306, 1138-1139.
42. PubChem, http://pubchem.ncbi.nlm.nih.gov/, 2009.