Discrimination between modes of toxic action of phenols using rule based methods

Molecular Diversity

Volumn 10, Issue 2, 2006, Pages 207-212

  Norinder, Ulf ^a   Lidén, Per ^b   Boström, Henrik ^c  

^a ASTRAZENECA R AND D   (Sweden)

^b Uppsala Science Park   (Sweden)

^c STOCKHOLM UNIVERSITY   (Sweden)

Author keywords

2 D descriptors; Ensembles; Phenols; Rule based modelling; Toxic action

Indexed keywords

NARCOTIC AGENT; PHENOL DERIVATIVE;

ACCURACY; ARTICLE; MODEL; PREDICTIVE VALIDITY; PRIORITY JOURNAL; TOXICITY;

EID: 33746238242     PISSN: 13811991     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11030-006-9019-3     Document Type: Article

References (18)

1. Garg, R., Kurup, A. and Hansch, C., Comparative QSAR: On the toxicology of the phenolic OH moiety, Crit. Rev. Toxicol., 31 (2001) 223-245.
2. Aptula, A.O., Netzeva, T.I., Valkova, I.V., Cronin, M.T.D., Schultz, T.W., Kühne, R. and Schüürmann, G., Multivariate Discrimination between Modes of Toxic Action of Phenols, Quant. Struct.-Act. Relat., 21 (2002) 12-22.
3. Ren, S., Ecotoxicity prediction using mechanism- and non-mechanism-based QSARs: A preliminary study, Chemosphere, 53 (2003) 1053-1065.
4. Schultz, T.W., Cronin, M.T.D., Walker, J.D. and Aptula, A.O., Quantitative structure-activity relationships (QSARs) in toxicology: A historical perspective, J. Mol. Struct. (Theochem), 622 (2003) 1-22.
5. Schultz, T.W., Lin, D.T., Wilke, T.S. and Arnold, L.M., Quantitative structure-activity relationships for the Tetrahymena pyriformis population growth endpoint: A mechanism of toxic action approach. In: Karcher, W., Devillers, J. (Eds.), Practical Applications of Quantitative Structure-Activity Relationships (QSAR) in Environmental Chemistry and Toxicology. Kluwer Academic, Dordrecht, Netherlands, 1990, pp. 241-262.
6. Cronin, M.T.D. and Dearden, J.C., Review: QSAR in toxicology. 1. Prediction of aquatic toxicity, Quant. Struct.-Act. Relat., 14 (1995) 1-7.
7. Michie, D., Spiegelhalter, D.J., Taylor, C.C. and Campbell, J., Machine Learning, Neural and Statistical Classification, Ellis Horwood, Upper Saddle River, USA, 1995.
8. Zmuidinavicius, D., Japertas, P., Petrauskas, A. and Didziapetris, D., Progress in Toxinformatics: The Challenge of Predicting Acute Toxicity, Curr. Top. Med. Chem., 3 (2003) 1301-1314.
9. Dearden, J.C., In silico prediction of drug toxicity, J. Comput.-Aid. Mol. Des., 17 (2003) 119-127.
10. Pudenz, S., Brüggemann, R. and Bartel, H.-G., QSAR of Ecotoxicological Data on the Basis of Data-Driven If-Then-Rules, Ecotox., 11 (2002) 337-342.
11. Greene, N., Computer systems for the prediction of toxicity: an update, Adv. Drug Deliv. Rev., 54 (2002) 417-431.
12. Karabunarlieva, S., Nikolovac, N., Nikolovd, N. and Mekenyana, O., Rule interpreter: A chemical language for structure-based screening, Journal of Molecular Structure (Theochem), 622 (2003) 53-62.
13. Breiman, L., Bagging Predictors, Machine Learning, 24 (1996) 123-140.
14. Breiman, L., Random Forests, Machine Learning, 45 (2001) 5-32.
15. Dietterich, T.G., Ensemble Methods in Machine Learning, Lecture Notes in Computer Science 1857 (2000) 1-15.
16. Olsson, T., Sherbukhin, V., SELMA, Synthesis and Structure Administration (SaSA), AstraZeneca R&D Mölndal, Sweden.
17. Compumine® Rule Discovery System™ (RDS™) version 2.1, Compumine AB, Uppsala Science Park, SE-751 83 Uppsala, Sweden, www.compumine.com
18. Quinlan, J. R., Induction of decision trees, Machine Learning, 1 (1986) 81-106.