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

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Summary

Rule-based ensemble modelling has been used to develop a model with high accuracy and predictive capabilities for distinguishing between four different modes of toxic action for a set of 220 phenols. The model not only predicts the majority class (polar narcotics) well but also the other three classes (weak acid respiratory uncouplers, pro-electrophiles and soft electrophiles) of toxic action despite the severely skewed distribution among the four investigated classes. Furthermore, the investigation also highlights the merits of using ensemble (or consensus) modelling as an alternative to the more traditional development of a single model in order to promote robustness and accuracy with respect to the predictive capability for the derived model.

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Abbreviations

RDS:

Rule Discovery System

QSAR:

quantitative structure-activity relationship

LDA:

linear discriminant analysis

PSA:

polar surface area

HMO:

Hückel molecular orbital

HOMO:

highest occupied molecular orbital

LUMO:

lowest unoccupied molecular orbital

E_HOMO:

energy of highest occupied molecular orbital

E_LUMO:

energy of lowest unoccupied molecular orbital

No_Hdon:

number of hydrogen bond donor centres

pKa:

negative decadic logarithm of the acidity constant

logKow:

decadic logarithm of the octanol/water partition coefficient

min dist DD:

minimum topological distance between two H-bond donors

max eV #3:

third highest eigen-value from BCUT (Burden-Chemical Abstracts-University of Texas) parameter matrix

nonpolar count/M:

number of non-polar atoms divided by molecular weight.

References

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  3. Ren, S., Ecotoxicity prediction using mechanism- and non-mechanism-based QSARs: A preliminary study, Chemosphere, 53 (2003) 1053–1065.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  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.

    Google Scholar 

  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.

    Article  CAS  Google Scholar 

  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.

    Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  9. Dearden, J.C., In silico prediction of drug toxicity, J. Comput.-Aid. Mol. Des., 17 (2003) 119–127.

    Article  CAS  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  11. Greene, N., Computer systems for the prediction of toxicity: an update, Adv. Drug Deliv. Rev., 54 (2002) 417–431.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  Google Scholar 

  13. Breiman, L., Bagging Predictors, Machine Learning, 24 (1996) 123–140.

    Google Scholar 

  14. Breiman, L., Random Forests, Machine Learning, 45 (2001) 5–32.

    Article  Google Scholar 

  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.

    Google Scholar 

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Authors and Affiliations

  1. AstraZeneca R&D Södertälje, SE-151 85, Södertälje, Sweden

    Ulf Norinder

  2. Compumine AB, Uppsala Science Park, SE-751 83, Uppsala, Sweden

    Per Lidén

  3. Department of Computer and Systems Sciences, Stockholm University and Royal Institute of Technology, Forum 100, SE-164 40, Kista, Sweden

    Henrik Boström

Authors
  1. Ulf Norinder
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  2. Per Lidén
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  3. Henrik Boström
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Correspondence to Ulf Norinder.

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Norinder, U., Lidén, P. & Boström, H. Discrimination between modes of toxic action of phenols using rule based methods. Mol Divers 10, 207–212 (2006). https://doi.org/10.1007/s11030-006-9019-3

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  • DOI: https://doi.org/10.1007/s11030-006-9019-3

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