ToxSci Advance Access originally published online on August 3, 2007
Toxicological Sciences 2007 99(2):532-544; doi:10.1093/toxsci/kfm185
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Categorical QSAR Models for Skin Sensitization based upon Local Lymph Node Assay Classification Measures Part 2: 4D-Fingerprint Three-State and Two-2-State Logistic Regression Models
,
,,1
* Laboratory of Molecular Modeling and Design (MC 781), College of Pharmacy, University of Illinois at Chicago, Chicago, Illinois 60612-7231
College of Pharmacy, SC09 5360, University of New Mexico, Albuquerque, New Mexico 87131-0001
The Chem21 Group, Inc., Lake Forest, Illinois 60045
Procter & Gamble Eurocor, B-1853 Strombeek-Bever, Belgium
|| The Procter & Gamble Company, Miami Valley Innovation Center, Cincinnati, Ohio 45253-8707
¶ Graduate Institute of Biomedical Engineering and Bioinformatics, Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan 106
1 To whom correspondence should be addressed at Graduate Institute of Biomedical Engineering and Bioinformatics, Department of Computer Science and Information Engineering, National Taiwan University, No. 1 Sec. 4, Roosevelt Road, Taipei, Taiwan 106. Fax: +886-2-236-28167. E-mail: yjtseng{at}csie.ntu.edu.tw.
Received May 4, 2007; accepted July 9, 2007
 |
Abstract |
|---|
Three and four state categorical quantitative structure–activity relationship (QSAR) models for skin sensitization have been constructed using data from the murine Local Lymph Node Assay studies. These are the same data we previously used to build two-state (sensitizer, nonsensitizer) QSAR models (Li et al., 2007, Chem. Res. Toxicol. 20, 114–128). 4D-fingerprint descriptors derived from the 4D-molecular similarity paradigm are used to generate these models. A training set of 196 and a test set of 22 structurally diverse compounds were used in this study. Logistic regression, and partial least square coupled logistic regression were used to build the models. The three-state QSAR model gives a classification accuracy of 73.4% for the training set and 63.6% for the test set, while the random average value of classification accuracy for any three-state data set is 33.3%. The two-2-state [four categories in total] QSAR model gives a classification accuracy of 83.2% for the training set and 54.6% for the test set, while the random average value of classification accuracy for any two-2-state data set is 25%. An analysis of the skin-sensitization models developed in this study, as well as the two-state QSAR models developed in our previous analysis, suggests that the "moderate" sensitizers may be the main source of limited model accuracy.
Key Words: skin sensitization; QSAR, logistic regression; 4D-fingerprints; categorical models.