PL EN


Preferences help
enabled [disable] Abstract
Number of results
Journal
2011 | 9 | 1 | 165-174
Article title

Analysis of the co-evolutions of correlations as a tool for QSAR-modeling of carcinogenicity: an unexpected good prediction based on a model that seems untrustworthy

Content
Title variants
Languages of publication
EN
Abstracts
EN
To validate QSAR models an external test set is increasingly used. However the definition of the compounds for the test set is still debated. We studied, co-evolutions of correlations between optimal descriptors and carcinogenicity (pTD50) for the subtraining, calibration, and test set. Weak correlations for the sub-training set are sometimes accompanied by quite good correlations for the external test set. This can be explained in terms of the probability theory and can help define a suitable test set. The simplified molecular input line entry system (SMILES) was used to represent the molecular structure. Correlation weights for calculating the optimal descriptors are related to fragments of the SMILES. The statistical quality of the model is: n=170, r2=0.6638, q2=0.6554, s=0.828, F=331 (sub-training set); n=170, r2=0.6609, r2pred=0.6520, s=0.825, F=331 (calibration set); and n=61, r2=0.7796, r2pred=0.7658, Rm2=0.7448, s=0.563, F=221 (test set). The calculations were done with CORAL software (http://www.insilico.eu/coral/). [...]
Publisher

Journal
Year
Volume
9
Issue
1
Pages
165-174
Physical description
Dates
published
1 - 2 - 2011
online
16 - 12 - 2010
Contributors
author
  • Institute of Pharmacologic Researches by Mario Negri, 20156, Milan, Italy
author
  • Institute of Pharmacologic Researches by Mario Negri, 20156, Milan, Italy
  • Institute of Pharmacologic Researches by Mario Negri, 20156, Milan, Italy
  • Departmen of Electronics and Information, Polytechnic Institute of Milan, 20133, Milan, Italy
References
  • [1] P.P. Roy, J.T. Leonard, K. Roy, Chemomet. Intell. Lab. 90, 31 (2008) http://dx.doi.org/10.1016/j.chemolab.2007.07.004[Crossref]
  • [2] W. Tong, Q. Xie, H. Hong, L. Shi, H. Fang, R. Perkins, Environ. Health. Persp. 112, 1249 (2004) http://dx.doi.org/10.1289/ehp.7125[Crossref]
  • [3] A.A. Toropov, A.P. Toropova, D.V. Mukhamedzhanova, I. Gutman, Indian J. Chem. 4A, 1545 (2005)
  • [4] G. Melagraki, A. Afantitis, H. Sarimveis, P.A. Koutentis, G. Kollias, O. Igglessi-Markopoulou, Mol. Divers. 13, 301 (2009) http://dx.doi.org/10.1007/s11030-009-9115-2[Crossref]
  • [5] E. Vicente, P.R. Duchowicz, E.A. Castro, A. Monge, J. Mol. Graph. Model. 28, 28 (2009) http://dx.doi.org/10.1016/j.jmgm.2009.03.004[Crossref]
  • [6] E. Benfenati (Ed.), Quantitative Structure-Activity Relationships (QSAR) for Pesticide Regulatory Purposes (Elsevier Science, Amsterdam, 2007)
  • [7] A.A. Toropov, A.P. Toropova, E. Benfenati, A. Manganaro, Mol. Divers. 13, 367 (2009) http://dx.doi.org/10.1007/s11030-009-9113-4[Crossref]
  • [8] A.A. Toropov, A.P. Toropova, E. Benfenati, Int. J. Mol. Sci. 10, 3106 (2009) http://dx.doi.org/10.3390/ijms10073106[Crossref]
  • [9] P.P. Roy, K. Roy, QSAR Comb. Sci. 27, 302 (2008) http://dx.doi.org/10.1002/qsar.200710043[Crossref]
Document Type
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.-psjd-doi-10_2478_s11532-010-0135-7
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.