Comparison of different approaches to define the applicability domain of QSAR models

Molecules

Volumn 17, Issue 5, 2012, Pages 4791-4810

  Sahigara, Faizan ^a   Mansouri, Kamel ^a   Ballabio, Davide ^a   Mauri, Andrea ^a   Consonni, Viviana ^a   Todeschini, Roberto ^a  

^a UNIVERSITY OF MILANO BICOCCA   (Italy)

Author keywords

Applicability Domain; Interpolation space; Model validation; QSAR

Indexed keywords

ALGORITHM; ARTICLE; CHEMICAL MODEL; COMPARATIVE STUDY; QUANTITATIVE STRUCTURE ACTIVITY RELATION; STATISTICAL MODEL;

ALGORITHMS; MODELS, CHEMICAL; MODELS, STATISTICAL; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 84861521242     PISSN: None     EISSN: 14203049     Source Type: Journal    
DOI: 10.3390/molecules17054791     Document Type: Article

References (27)

1. Netzeva, T.I.; Worth, A.; Aldenberg, T.; Benigni, R.; Cronin, M.T.D.; Gramatica, P.; Jaworska, J.S.; Kahn, S.; Klopman, G.; Marchant, C.A.; et al. Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships. The report and recommendations of ECVAM Workshop 52. Altern. Lab. Anim. 2005, 33, 155-173.
2. Jaworska, J.; Nikolova-Jeliazkova, N.; Aldenberg, T. QSAR applicabilty domain estimation by projection of the training set descriptor space: A review. Altern. Lab. Anim. 2005, 33, 445-459.
3. Dimitrov, S.; Dimitrova, G.; Pavlov, T.; Dimitrova, N.; Patlewicz, G.; Niemela, J.; Mekenyan, O.A. Stepwise approach for defining the applicability domain of SAR and QSAR models. J. Chem. Inf. Model. 2005, 45, 839-849.
4. REACH. European Community Regulation on chemicals and their safe use. Available online: http://ec.europa.eu/environment/chemicals/reach/reach-intro. htm (accessed on 3 February 2012
5. Worth, A.P.; Bassan, A.; Gallegos, A.; Netzeva, T.I.; Patlewicz, G.; Pavan, M.; Tsakovska, I.; Vracko, M. The Characterisation of (Quantitative) Structure-Activity Relationships: Preliminary Guidance. ECB Report EUR 21866 EN, European Commission, Joint Research Centre; Ispra, Italy, 2005; p. 95.
6. OECD. Quantitative Structure-Activity Relationships Project [(Q)SARs]. Available online: http://www.oecd.org/document/23/0,3746,en-2649-34377-33957015- 1-1-1-1,00.html (accessed on 3 February 2012
7. Worth, A.P.; van Leeuwen, C.J.; Hartung, T. The prospects for using (Q)SARs in a changing political environment: high expectations and a key role for the Commission's Joint Research Centre. SAR QSAR Environ. Res. 2004, 15, 331-343.
8. Nikolova-Jeliazkova, N.; Jaworska, J. An approach to determining applicability domains for QSAR group contribution models: an analysis of SRC KOWWIN. Altern. Lab. Anim. 2005, 33, 461-470.
9. Sheridan, R.; Feuston, R.P.; Maiorov, V.N.; Kearsley, S. Similarity to molecules in the training set is a good discriminator for prediction accuracy in QSAR. J. Chem. Inf. Comp. Sci. 2004, 44, 1912-1928.
10. Zhao, C.; Boriani, E.; Chana, A.; Roncaglioni, A.; Benfenati, E. A new hybrid QSAR model for predicting bioconcentration factor (BCF). Chemosphere 2008, 73, 1701-1707.
11. Lombardo, A.; Roncaglioni, A.; Boriani, E.; Milan, C.; Benfenati, E. Assessment and validation of the CAESAR predictive model for bioconcentration factor (BCF) in fish. Chem. Cent. J. 2010, 4 (Suppl 1), doi:10.1186/1752-153X-4- S1-S1.
12. Meylan, W.M.; Howard, P.H.; Aronson, D.; Printup, H.; Gouchie, S. Improved Method for Estimating Bioconcentration Factor (BCF) from Octanol-Water Partition Coefficient, 2nd Update; SRC TR-97-006; Syracuse Research Corp., Environmental Science Center: North Syracuse, NY, USA, 1997; Prepared for: Robert S. Boethling, EPA-OPPT.
13. Meylan, W.M.; Howard, P.H.; Boethling, R.S.; Aronson, D.; Printup, H.; Gouchie, S. Improved method for estimating bioconcentration/bioaccumulation factor from octanol/water partition coefficient. Environ. Toxicol. Chem. 1999, 18, 664-672.
14. MATLAB. The Language of Technical Computing. Available online: http://www.mathworks.com/products/matlab/ (accessed on 3 February 2012
15. Wold, S.; Esbensen, K.; Geladi, P. Principal component analysis. Chemometr. Intell. Lab. 1987, 2, 37-52.
16. Preparata, F.P.; Shamos, M.I. Convex Hulls: Basic Algorithms. In Computational Geometry: An Introduction; Preparata, F.P., Shamos, M.I., Eds.; Springer-Verlag: New York, NY, USA, 1991; pp. 95-148.
17. Tropsha, A.; Gramatica, P.; Gombar, V. The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR Models. QSAR Comb. Sci. 2003, 22, 69-77.
18. Jouan-Rimbaud, D.; Bouveresse, E.; Massart, D.L.; de Noord, O.E. Detection of prediction outliers and inliers in multivariate calibration. Anal. Chim. Acta 1999, 388, 283-301.
19. Forina, M.; Armanino, C.; Leardi, R.; Drava, G. A class-modelling technique based on potential functions. J. Chemometr. 1991, 5, 435-453.
20. Tong, W.; Hong, H.; Fang, H.; Xie, Q. Perkins, R. Decision forest: Combining the predictions of multiple independent decision tree models. J. Chem. Inf. Comput. Sci. 2003, 43, 525-531.
21. Tong, W.; Hong, H.; Xie, Q.; Xie, L.; Fang, H.; Perkins, R. Assessing QSAR limitations: A regulatory perspective. Curr. Comput. Aid. Drug Des. 2004, 1, 65-72.
22. Wan, C.; Harrington, P.B. Self-configuring radial basis function neural networks for chemical pattern recognition. J. Chem. Inf. Comput. Sci. 1999, 39, 1049-1056.
23. DRAGON (Software for Molecular Descriptor Calculations). Talete srl, Milano, Italy. Available online: http://www.talete.mi.it (accessed on 3 February 2012
24. Consonni, V.; Ballabio, D.; Todeschini, R. Comments on the definition of the Q2 parameter for QSAR validation. J. Chem. Inf. Model. 2009, 49, 1669-1678.
25. Consonni, V.; Ballabio, D.; Todeschini, R. Evaluation of model predictive ability by external validation techniques. J. Chemometr. 2010, 24, 104-201.
26. Tetko, I.V.; Sushko, I.; Pandey, A.K.; Zhu, H.; Tropsha, A.; Papa, E.; Oberg, T.; Todeschini, R.; Fourches, D.; Varnek, A. Critical assessment of QSAR models of environmental toxicity against Tetrahymena pyriformis: Focusing on applicability domain and overfitting by variable selection. J. Chem. Inf. Model. 2008, 48, 1733-1746.
27. Weaver, S.; Gleeson, M.P. The importance of the domain of applicability in QSAR modeling. J. Mol. Graph. Model. 2008, 26, 1315-1326.