Toxicity in allelopathy: In silico approach

Allelopathy: A Physiological Process with Ecological Implications

Volumn , Issue , 2006, Pages 105-126

  Lo Piparo, E ^a   Fratev, F ^a   Mazzatorta, P ^a   Smiesko, M ^a   Benfenati, E ^a  

^a MARIO NEGRI INSTITUTE FOR PHARMACOLOGICAL RESEARCH   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84919596851     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/1-4020-4280-9_5     Document Type: Chapter

References (46)

1. Ashby, J. (1994) International Commission for Protection Against Environmental Mutagens and Carcinogens. Two millions rodent carcinogens: the role of SAR and QSAR in their detection. Mutat. Res. 305, 3-2.
2. Benfenati, E., and Gini, G. (1997) Computational Predictive Programs (expert systems) in toxicology. Toxicology, 119(3):213-25.
3. Berman, H. M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T. N., Weissig, H., Shindyalov, I. N. and Bourne, P. E. (2000) The Protein Data Bank. Nucleic Acids Res. 28, 235-242.
4. Chen, S., Cowan, C. F. N. and Grant P. M. (1991) "Orthogonal Least Squares Learning Algorithm for Radial Basis Function Networks", IEEE Transactions on Neural Networks, vol. 2, no. 2, pp. 302-309.
5. Commission of the European Communities (2001) White Paper on Strategy for a future Chemicals Policy, Bruxelles, 27 February 2001 COM88 final.
6. Cramer, Ch. J. (2004) Essentials of Computational Chemistry: Theories and Models. John Wiley and Sons. Canada.
7. Cramer, R. D., Patterson, D. E. and Bunce, J. D. (1988) J. Amer. Chem. Soc. 110, 5959-5967.
8. Cramer, R. D., Patterson, D. E. and Bunce, J. D. (1989) Prog. Clin. Biol. Res. 291, 161-165.
9. Dearden, J. C., Barratt, M. D., Benigni, R., Bristol, D. W., Combes, R. D., Cronin, M. T., Judson, P. N., Payne, M. P., Richard, A. M., Tichy, M., Worth, A. P. and Yourick, J. J. (1997) The development and validation of expert systems for predicting toxicity. ATLA, 25, 223-252.
10. Egan, W. J., Morgan, S. L. (1998) Outlier detection in multivariate analytical chemical data. Anal. Chem. 70, 2372-2379.
11. Eriksson L., Johansson E., Kettaneh-Wold N. and Wold S. (2001) Multi- and megavariate data analysis: Principles and applications. Umeå: Umetrics.
12. Gini, G., Lorenzini, M., Benfenati, E., Grasso P. and Bruschi, M. (1999) Predictive carcinogenicity: a model for aromatic compounds, with nitrogen-containing substituents, based on molecular descriptors using an artificial neural network. J. Chem. Inf. Comput. Sci. 39, 1076-1080.
13. Golbraikh, A. and Tropsha. A. (2002) Beware of q2! Journal of Molecular Graphics and Modelling. 20, 269-276.
14. Golbraikh, A., Bernard, P. and Chretien, J. R. (2000) Validation of protein-based alignment in 3D quantitative structure-activity relationships with CoMFA models. Eur. J. Med. Chem. 35:1, 123-136.
15. Goldberg, D. E. (1989) Genetic Algorithms in Search, Optimization & Machine Learning; Addison-Wesley: New York.
16. Goodford, P. J (1985) GRID,. Molecular Discovery Ltd, University of Oxford. Oxford, UK.
17. Goodsell, D. S., Morris, G. M. and Olson, A. J. (1996) Docking of Flexible Ligands: Applications of AutoDock, J. Mol. Recognition 1996, 9, 1-5.
18. Hasegawa, K., Miyashita, Y., Funatsu, K. (1997) GA strategy for variable selection in QSAR studies: GA-based PLS analysis of calcium channel antagonists. J. Chem. Inf. Comput. Sci. 37. 306-310.
19. Hawkins, D. M. (2004) The Problem of Overfitting, J. Chem. Inf. Comput. Sci. 44, 1-12.
20. Hawkins, D. M., Basak, S. C. and Mills, D. (2003) Assessing Model Fit by Cross-Validation. J. Chem. Inf. Comput. Sci. 43, 579-586.
21. Herman, W. (1985) "Partial Least Squares", in Samuel Kotz and Norman L. Johnson, eds., Encyclopedia of Statistical Sciences, Vol. 6, New York: Wiley. Pp. 581-591.
22. Holland, J. (1975) Adaptation in Natural and Artificial Systems. The University of Michigan Press: Ann Arbor.
23. Höskuldsson, A. (1988) PLS regression methods. J. Chemometrics. 2(3):211-228.
24. Jiang, B., Xiong, W. N., Yang, C. G., Jiang, H. L., Cheng, F. and Chen, K. X. (2002) Structure-based 3D QSAR analysis of marine indole alkaloids. Bioorg. Med. Chem. 10:8, 2775-2778.
25. Kinnear, K. E. (1994) Advances in Genetic Programming; MIT Press: Cambridge, MA.
26. Kohonen, T. (1987) Self-Organization and Associative Memory, 2nd Edition, Berlin: Springer-Verlag.
27. Lengauer, T. and Rarey, M. (1996) Curr. Opin. Struc. Biol. 6, 402-406.
28. Martin, Y. C., Kim, K. H. and Liu, C. T. (1996) Comparative Molecular Field Analysis: CoMFA. In Advances in Quant. Str.-Property Relationships. 1, 1-52. JAI Press.
29. Mazzatorta, P., Vracko, M. and Benfenati, E. (2003) ANVAS: Artificial neural variables adaptation system for descriptor selection. Journal of Computer-Aided Molecular Design 17, 335-346.
30. McCullochm, W. and Pitts, W. (1943) A Logical Calculus of Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biophysics 5, 115-133.
31. Morris, G. M., Goodsell, D. S., Halliday, R. S., Huey, R., Hart, W. E., Belew, R. K. and Olson, A. J. (1998) Automated Docking Using a Lamarckian Genetic Algorithm and Empirical Binding Free Energy Function. J. Comput. Chem. 19, 1639-1662.
32. Naes, M. H. (1989) T. Multivariate Calibration. Wiley Canada.
33. Omen, G. S. (1995) Assessing the Risk Assessment Paradigm. Toxicology, 102, 23-28.
34. Rosenblatt, F. (1961) Principles of Neurodynamics. Spartan Press. Washington D. C. USA.
35. Rumelhart, D. E., Hinton, G. E. and Williams, R. J. (1986) Learning internal representations by error propagation. D. Rumelhart and J. McClelland, editors. Parallel Data Processing, Vol. 1, Chapter 8, the M. I. T. Press, Cambridge, MA. Pp. 318-362.
36. Sippl, W. (2002) Development of biologically active compounds by combining 3D QSAR and structurebased design methods. J. Computer-Aided Mol. Des. 16:11, 825-830.
37. Sippl, W., Contreras, J. M., Parrot, I., Rival, Y. M. and Wermuth, C. G. Structure-based 3D QSAR and design of novel acetylcholinesterase inhibitors. J. Computer - Aided Mol. Des. 15:5, 395-410.
38. Tomlin, C. D. S. (2000) The Pesticide Manual. 12th Edition, British Crop Protection Council. UK.
39. Topliss, J. G. and Costello, R. J. (1972) Chance Correlation in Structure-Activity Studies Using Multiple Regression Analysis. Journal of Medicinal Chemistry, 15, 10, 1066-1068.
40. Topliss, J. G. and Edwards, R. P (1979) Chance Factors in Studies of Quantitative Structure-Activity Relationships. Journal of Medicinal Chemistry, 22, 10, 1238-1244.
41. Tropsha, A., Grammatica, P., Gombar, V. K. (2003). The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models. QSAR Comb. Sci. 22, 69-77.
42. Villemin, D., Cherqaoui, D., and Mesbash, A. (1994) Predicting Carcinogenicity of Polycyclic Aromatic Hydrocarbons from Back-Propagation Neural Network. J. Chem. Inf. Comput. Sci., 34, 1288-1293.
43. Walker, J. D. (2003) QSARs for Toxicity Screening: Current Practices, chapter 5 QSARs for Pollution Prevention, Toxicity Screening, Risk Assessment, and Web Applications. SETAC.
44. Waller, C. L., Evans, M. V. and McKinney, J. D. (1996) Modeling the cytochrome P450-mediated metabolism of chlorinated volatile organic compounds. Drug. Metab. Dispos. 24:2, 203-210.
45. Williams, P. A., Cosme, J., Sridhar, V., Johnson, E. F. and Mcree, D. E. (2000) Mammalian Microsomal Cytochrome P450 Monooxygenase: Structural Adaptations for Membrane Binding and Functional Diversity. Mol. Cell. 5, 121-131.
46. Yeung P. K. K., Wong, F. T. W. and Wong, J. T. Y. (2002) Mimosine, the Allelochemical from the Leguminous Tree Leucaena leucocephala, Selectively Enhances Cell Proliferation in Dinoflagellates. Appl. Envir. Microbiol. 68(10):5160-5163.