Skip to main content
SpringerLink
Log in

Evaluating the applicability domain in the case of classification predictive models for carcinogenicity based on the counter propagation artificial neural network

  • Published:
Journal of Computer-Aided Molecular Design Aims and scope Submit manuscript

Abstract

The applicability domain (AD) of models developed for regulatory use has attached great attention recently. The AD of quantitative structure–activity relationship (QSAR) models is the response and chemical structure space in which the model makes predictions with a given reliability. The evaluation of AD of regressions QSAR models for congeneric sets of chemicals can be find in many papers and books while the issue about metrics for the evaluation of an AD for the non-linear models (like neural networks) for the diverse set of chemicals represents the new field of investigations in QSAR studies. The scientific society is standing before the challenge to find out reliable way for the evaluation of an AD of non linear models. The new metrics for the evaluation of the AD of the counter propagation artificial neural network (CP ANN) models are discussed in the article: the Euclidean distances between an object (molecule) and the corresponding excited neuron of the neural network and between an object (molecule) and the representative object (vector of average values of descriptors). The investigation of the training and test sets chemicals coverage in the descriptors space was made with the respect to false predicted chemicals. The leverage approach was used to compare non linear (CP ANN) models with linear ones.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. OECD (2007) Guidance document on the validation of (quantitative) structure-activity relationships [(Q)SAR] MODELS. OECD Environment Health and Safety Publications, Series on Testing and Assessment No. 69, [http://appli1.oecd.org/olis/2007doc.nsf/linkto/env-jm-mono(2007)2]

  2. Netzeva TI, Worth AP, Aldenberg T, Benigni R, Cronin MTD, Gramatica P, Jaworska JS et al (2005) Current status of methods for defining the applicability domain of (quantitative) structure–activity relationships. The report and recommendations of ECVAM Workshop 52. ATLA 33:155–173

    CAS  Google Scholar 

  3. Jaworska J, Nikolova-Jeliazkova N, Aldenberg T (2005) QSAR applicability domain estimation by projection of the training set descriptor space: a review. Altern Lab Anim 33(5):445–459, ISSN: 0261-1929

    Google Scholar 

  4. Tetko IV, Sushko I, Pandey AK, Zhu H, Tropsha A, Papa E, Oberg T, Todeschini R, Fourches D, Varnek A (2008) Critical assessment of QSAR models of environmental toxicity against Tetrahymena pyriformis: focusing on applicability domain and overfitting by variable selection. J Chem Inf Model 48(9):1733–1746. doi:10.1021/ci800151m

    Article  CAS  Google Scholar 

  5. Dimitrov SD, Dimitrova GD, Pavlov TS, Dimitrova N, Patlewicz GY, Niemela J, Mekenyan OG (2005) A stepwise approach for defining the applicability domain of SAR and QSAR models. J Chem Inf Model 45:839–849. doi:10.1021/ci0500381

    Article  CAS  Google Scholar 

  6. Duda RO, Hart PE, and Stork DG (2001) Pattern Classification. Wiley, New York: 654. doi: 10.1007/s00357-007-0015-9

  7. Nikolova N, Jaworska J (2003) Approaches to measure chemical similarity—a review. QSAR Comb Sci 22(9–10):1006–1026. doi:10.1002/qsar.200330831

    Article  Google Scholar 

  8. Haykin S (1998) Neural networks: a comprehensive foundation, 2nd edn. Prentice Hall. Inc, Englewood Cliffs

    Google Scholar 

  9. Benfenati E, Benigni R, DeMarini D, Helma C, Kirkland D, Martin TM, Mazzatorta P, Ouedrago-Arras G, Richard AM, Schilter B, Schoonen WG, Snyder RD, Yang C (2009) Predictive models for carcinogenicity: frameworks, state-of-the-art, and perspectives. J Environ Sci Health C 27:57–90. doi:10.1080/10590500902885593

    Article  CAS  Google Scholar 

  10. Walker JD, Lars Carlsen L, Jaworska J (2003) Improving opportunities for regulatory acceptance of QSARS: the importance of model domain, uncertainty, validity and predictability. QSAR Comb Sci 22(3):346–350. doi:10.1002/qsar.200390024

    Article  CAS  Google Scholar 

  11. Eriksson L, Jaworska J, Worth AP, Cronin MT, McDowell RM, Gramatica P (2003) Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs. Environ Health Perspect 111:1361–1375. doi:10.1289/ehp.5758

    Article  CAS  Google Scholar 

  12. Gramatica P, Pilutti P, Papa E (2003) Predicting the NO″3 radical tropospheric degradability of organic pollutants by theoretical molecular descriptors. Atmos Environ 37:3115–3124. doi:10.1016/S1352-2310(03)00293-0

    Article  CAS  Google Scholar 

  13. Caesar project web page http://www.caesar-project.eu/software

  14. Fjodorova N, Vračko M, Tušar M, Jezierska A, Novič M, Kühne R, Schüürmann G (2010) Quantitative and qualitative models for carcinogenicity prediction for non-congeneric chemicals using CP ANN method for regulatory uses. Mol Divers 14(3):581–594. doi:10.1007/s11030-009-9190-4

    Article  CAS  Google Scholar 

  15. Fjodorova N, Vračko M, Novič M, Roncaglioni A, Benfenati E (2010) New public QSAR model for carcinogenicity. Chem Cent J 4(Suppl 1):S3, http://www.journal.chemistrycentral.com/content/4/S1/S3. doi:10.1186/1752-153X-4-S1-S3

  16. Distributed Structure-Searchable Toxicity (DSSTox) Public Database Network http://www.epa.gov/ncct/dsstox/sdf_cpdbas.html

  17. Zupan J, Gasteiger J (1999) Neural networks in chemistry and drug design. Wiley-VCH Verlag GmbH, Weinheim

    Google Scholar 

  18. Maran E, Novic M, Barbieri P, Zupan J (2004) Application of counterpropagation artificial neural network for modelling properties of fish antibiotics. SAR QSAR Environ Res 15(5–6):469–480. doi:10.1080/10629360412331297461

    Article  CAS  Google Scholar 

  19. Tropsha A, Gramatica P, Gombar VK (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. doi:10.1002/qsar.200390007

    Article  CAS  Google Scholar 

  20. Gramatica P (2007) Principles of QSAR models validation: internal and external. QSAR Comb Sci 26(5):694–701. doi:10.1002/qsar.20061015

    Article  CAS  Google Scholar 

  21. Saliner AG, Patlewicz G, Worth AP (2005) A similarity based approach for chemical category classification. JRS report EUR 21867 EN:1–44

    Google Scholar 

  22. Benigni R, Bossa C, Jeliazkova N, Netzeva TI, Worth AP (2008) The Benigni/Bossa rulebase for mutagenicity and carcinogenicity—a module of Toxtree. EUR 23241 EN:1–70

    Google Scholar 

Download references

Acknowledgments

Authors thank for the European Commission for the financial support under project CAESAR (SSPI-022674), the Slovenian Ministry of Higher Education, Science and Technology (grant P1-017).

Author information

Authors and Affiliations

  1. National Institute of Chemistry, Hajdrihova 19, SI-1001, Ljubljana, Slovenia

    Natalja Fjodorova & Marjana Novič

  2. Institute for Pharmacological Research “Mario Negri”, Via La Masa 19, 20156, Milan, Italy

    Alessandra Roncaglioni & Emilio Benfenati

Authors
  1. Natalja Fjodorova
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marjana Novič
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alessandra Roncaglioni
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Emilio Benfenati
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Natalja Fjodorova.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOC 1411 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Fjodorova, N., Novič, M., Roncaglioni, A. et al. Evaluating the applicability domain in the case of classification predictive models for carcinogenicity based on the counter propagation artificial neural network. J Comput Aided Mol Des 25, 1147–1158 (2011). https://doi.org/10.1007/s10822-011-9499-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10822-011-9499-9

Keywords

Search

Navigation