In silico screening of estrogen-like chemicals based on different nonlinear classification models

Journal of Molecular Graphics and Modelling

Volumn 26, Issue 1, 2007, Pages 135-144

  Liu, Huanxiang ^a   Papa, Ester ^a   Walker, John D ^b   Gramatica, Paola ^a  

^a UNIVERSITY OF INSUBRIA   (Italy)

^b U S ENVIRONMENTAL PROTECTION AGENCY   (United States)

Author keywords

Classification; CP ANN; Endocrine disruptor; Environmental estrogens; LS SVM; QSAR

Indexed keywords

HEALTH RISKS; HORMONES; LEAST SQUARES APPROXIMATIONS; MATHEMATICAL MODELS; MOLECULAR STRUCTURE; SUPPORT VECTOR MACHINES;

ESTROGEN-SIMULATING ACTIVITY; NONLINEAR CLASSIFICATION MODELS; QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP (QSAR); SILICO SCREENING;

ENDOCRINOLOGY;

ENDOCRINE DISRUPTOR; ENVIRONMENTAL CHEMICAL; ESTROGEN; ESTROGEN RECEPTOR;

ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPUTER MODEL; HIGH THROUGHPUT SCREENING; NONLINEAR SYSTEM; PRIORITY JOURNAL; QUANTITATIVE STRUCTURE ACTIVITY RELATION; SUPPORT VECTOR MACHINE;

ALGORITHMS; ANIMALS; ARTIFICIAL INTELLIGENCE; DRUG EVALUATION, PRECLINICAL; ENDOCRINE DISRUPTORS; ESTRADIOL CONGENERS; FEMALE; HUMANS; KNOWLEDGE BASES; NEURAL NETWORKS (COMPUTER); NONLINEAR DYNAMICS; RATS; RECEPTORS, ESTROGEN; SOFTWARE;

EID: 34250824089     PISSN: 10933263     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jmgm.2007.01.003     Document Type: Article

References (42)

1. Hileman B. Hormone disrupter research expands. Chem. Eng. News 75 (1997) 24-25
2. Fang H., Tong W., and Sheehan D. QSARs in receptor-mediated effects: the nuclear receptor superfamily. J. Mol. Struct. (THEOCHEM) 622 (2003) 113-125
3. Saliner A.G., Amat L., Carbó-Dorca R., Schultz T.W., and Cronin M.T.D. Molecular quantum similarity analysis of estrogenic activity. J. Chem. Inf. Comput. Sci. 43 (2003) 1166-1176
4. Hong H., Tong W., Fang H., Shi L., Xie Q., Wu J., Perkins R., Walker J.D., Branham W., and Sheehan D.M. Prediction of estrogen receptor binding for 58, 000 chemicals using an integrated system of a tree-based model with structural alerts. Environ. Health Perspect. 110 (2002) 29-36
5. Shi L., Tong W., Fang H., Xie Q., Hong H., Perkins R., Wu J., Tu M., Blair R., Branham W.S., Waller C., Walker J., and Sheehan D.M. An integrated "4-phase" approach for setting endocrine disruptor screening priorities-phase I and II predictions of estrogen receptor binding affinity. SAR QSAR Environ. Res. 13 (2002) 69-88
6. http://europa.eu.int/comm/environment/chemicals/reach.html
7. Tong W., Welsh W.J., Shi L., Fang H., and Perkins R. Structure-activity relationship approaches and applications. Environ. Toxicol. Chem. 22 (2003) 1680-1695
8. Asikainen A., Kolehmainen M., Ruuskanen J., and Tuppurainen K. Structure-based classification of active and inactive estrogenic compounds by decision tree, LVQ and kNN methods. Chemosphere 62 (2006) 658-673
9. Chen J.J., Tsai C.-A., Moon H., Ahn H., Young J.J., and Chen C.H. Decision threshold adjustment in class prediction. SAR QSAR Environ. Res. 17 (2006) 337-352
10. Li H., Ung C.Y., Yap C.W., Xue Y., Li Z.R., and Chen Y.Z. Prediction of estrogen receptor agonists and characterization of associated molecular descriptors by statistical learning methods. J. Mol. Graph. Model. 25 (2006) 313-323
11. Yasri A., and Hartsough D. Toward an optimal procedure for variable selection and QSAR model building. J. Chem. Inf. Comput. Sci. 41 (2001) 1218-1227
12. Hou T.J., Wang J.M., Liao N., and Xu X.J. Application of genetic algorithms on the structure-activity relationship analysis of some cinnamaides. J. Chem. Inf. Comput. Sci. 39 (1999) 775-781
13. Hasegawa K. GA strategy for variable selection in QSAR studies: application of GA-based region selection to a 3D-QSAR study of acetylcholinesterase inhibitors. J. Chem. Inf. Comput. Sci. 39 (1999) 112-120
14. Leardi R., Boggia R., and Terrile M. Genetic algorithms as a strategy for feature selection. J. Chemom. 6 (1992) 267-281
15. Sharaf M.A., Illman D.L., and Kowalski B.R. Chemometrics (1986), Wiley, New York
16. Dayhof J. Neural Network Architectures, An Introduction (1990), Van Nostrand Reinhold, New York p. 192
17. Suykens J.A.K., and Vandewalle J. Least squares support vector machine classifiers. Neural Process. Lett. 9 (1999) 293-300
18. Hecht-Nielsen R. Application of counter-propagation networks. Neural Netw. 1 (1988) 131-140
19. Jezierska A., Vracko M., and Basak S.C. Counter-propagation artificial neural network as a tool for the independent variable selection: structure-mutagenicity study on aromatic amines. Mol. Divers. 8 (2004) 371-377
20. Cortes C., and Vapnik V. Support-vector networks. Mach. Learn. 20 (1995) 273-297
21. Schölkopf B., Burges C., and Smola A. Advances in Kernel Methods-Support Vector Learning (1999), MIT Press, Cambridge, MA
22. Cristianini N., and Shawe-Taylor J. An Introduction to Support Vector Machines (2000), Cambridge University Press, Cambridge, UK
23. URL:. http://www.kernel-machines.org/
24. Belousov A.I., Verzakov S.A., and Von Frese J. A flexible classification approach with optimal generalization performance: support vector machines. Chemom. Intell. Lab. Syst. 64 (2002) 15-25
25. Morris C.W., Autret A., and Boddy L. Support vector machines for identifying organisms-a comparison with strongly partitioned radial basis function networks. Ecol. Model. 146 (2001) 57-67
26. Liu H.X., Zhang R.S., Luan F., Yao X.J., Liu M.C., Hu Z.D., and Fan B.T. Diagnosing breast cancer based on support vector machines. J. Chem. Inf. Comput. Sci. 43 (2003) 900-907
27. Liu H.X., Zhang R.S., Yao X.J., Liu M.C., Hu Z.D., and Fan B.T. Prediction of isoelectric point of amino acid based on GA-PLS and SVMs. J. Chem. Inf. Comput. Sci. 44 (2004) 161-169
28. Liu H.X., Zhang R.S., Yao X.J., Liu M.C., Hu Z.D., and Fan B.T. QSAR and classification models of a novel series of COX-2 selective inhibitors: 1, 5-diarylimidazoles based on support vector machines. J. Comput. Aided Mol. Des. 18 (2004) 389-399
29. Liu H.X., Papa E., and Gramatica P. QSAR prediction of estrogen activity for a large set of diverse chemicals under the guidance of OECD principles. Chem. Res. Toxicol. 19 (2006) 1540-1548
30. HyperChem. Release 7.03 for Windows. Molecular Modeling System (2002), Hypercube, Inc., Gainesville, FL, USA
31. Todeschini R., Consonni V., Mauri A., and Pavan M. DRAGON-Software for the Calculation of Molecular Descriptors. Version 5.4 for Windows (2006), Talete srl, Milan, Italy
32. Todeschini R., and Consonni V. Handbook of Molecular Descriptors (2000), Wiley-VCH, Weinheim, Germany
33. Rogers D., and Hopfinger A.J. Application of genetic function approximation to quantitative structure-activity relationships and quantitative structure-property relationships. J. Chem. Inf. Comput. Sci. 34 (1994) 854-866
34. Papa E., Villa F., and Gramatica P. Statistically validated QSARs and theoretical descriptors for the modelling of the aquatic toxicity of organic chemicals in Pimephales promelas (Fathead Minnow). J. Chem. Inf. Model. 45 (2005) 1256-1266
35. Gramatica P., Pilutti P., and Papa E. Validated QSAR prediction of OH tropospheric degradation of VOCs: splitting into training-test sets and consensus modeling. J. Chem. Inf. Comput. Sci. 44 (2004) 1794-1802
36. Tropsha A., Gramatica P., and Gombar V.K. The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb. Sci. 22 (2003) 69-76
37. Fang H., Tong W., Shi L., Blair R., Perkins R., Branham W.S., Dial S.L., Moland C.L., and Sheehan D.M. Structure activity relationship for a large diverse set of natural, synthetic and environmental chemicals. Chem. Res. Toxicol. 14 (2001) 280-294
38. Brzozowski A.M., Pike A.C., Dauter Z., Hubbard R.E., Bonn T., Engstrom O., Ohman L., Greene G.L., Gustafsson J.A., and Carlquist M. Molecular basis of agonism and antagonism in the oestrogen receptor. Nature 389 (1997) 753-758
39. Shiau A.K., Barstad D., Loria P.M., Cheng L., Kushner P.J., Agard D.A., and Greene G.L. The structural basis of estrogen receptor/coactivator recognition and the antagonism of this interaction by tamoxifen. Cell 95 (1998) 927-937
40. Estrada E. Edge adjacency relationships and a novel topological index related to molecular volume. J. Chem. Inf. Comput. Sci. 35 (1995) 31-33
41. Estrada E. Edge adjacency relationships in molecular graphs containing heteroatoms: a new topological index related to molar volume. J. Chem. Inf. Comput. Sci. 35 (1995) 701-707
42. Wang W.J., Xu Z.B., Lu W.Z., and Zhang X.Y. Determination of the spread parameter in the Gaussian kernel for classification and regression. Neurocomputing 55 (2003) 643