Prediction of Organ Toxicity Endpoints by QSAR Modeling Based on Precise Chemical-Histopathology Annotations
Article first published online: 27 JUN 2012
© 2012 John Wiley & Sons A/S
Chemical Biology & Drug Design
Volume 80, Issue 3, pages 406–416, September 2012
How to Cite
Myshkin, E., Brennan, R., Khasanova, T., Sitnik, T., Serebriyskaya, T., Litvinova, E., Guryanov, A., Nikolsky, Y., Nikolskaya, T. and Bureeva, S. (2012), Prediction of Organ Toxicity Endpoints by QSAR Modeling Based on Precise Chemical-Histopathology Annotations. Chemical Biology & Drug Design, 80: 406–416. doi: 10.1111/j.1747-0285.2012.01411.x
- Issue published online: 23 JUL 2012
- Article first published online: 27 JUN 2012
- Accepted manuscript online: 14 MAY 2012 04:41PM EST
- Received 20 December 2011, revised 28 March 2012 and accepted for publication 8 May 2012
Figures S1–S8. Common substructures identified in by the JKlustor 5.9.0 utility from ChemAxon for QSAR models ‘Hepatotoxicity’, ‘Liver Weight Gain’, ‘Liver Necrosis’, ‘Liver Lipid Accumulation’, ‘Nephrotoxicity’, ‘Nephron Injury’, ‘Kidney Weight Gain’, and ‘Kidney Necrosis’ respectively.
Table S1. Example of all descriptors for acetamoniphen.
Table S2. The lowest and the highest number of leaves among 50 decision trees in each model.
Table S3. Effect of different ChemTree parameters on QSAR predictions using Liver relative weight gain model as an example.
Table S4. Cross-validation error of correctly predicted toxicants averaged over 10 random subsamples of training and test sets ± standard deviation.
Table S5. Number of times each descriptor is used in 50 trees for each toxicity model.
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