Abstract
A modified version of the CORAL software (http://www.insilico.eu/coral) allows building up the classification model for the case of the Yes/No data on the anti-sarcoma activity of organic compounds. Three random splits into the sub-training, calibration, and test sets of the data for 3017 compounds were examined. The performance of the proposed approach is satisfactory. The average values of the statistical characteristics for external test set on three random splits are as follows: n=1173-1234, sensitivity = 0.8903±0.0390, specificity = 0.9869±0.0013, and accuracy = 0.9759±0.0043. Mechanistic interpretation of the suggested model is discussed.
Keywords: QSAR, Monte Carlo method, CORAL software, classification model, anti-sarcoma activity
Current Topics in Medicinal Chemistry
Title:CORAL: Classification Model for Predictions of Anti-Sarcoma Activity
Volume: 12 Issue: 24
Author(s): A.A. Toropov, A.P. Toropova, E. Benfenati, G. Gini, D. Leszczynska and J. Leszczynski
Affiliation:
Keywords: QSAR, Monte Carlo method, CORAL software, classification model, anti-sarcoma activity
Abstract: A modified version of the CORAL software (http://www.insilico.eu/coral) allows building up the classification model for the case of the Yes/No data on the anti-sarcoma activity of organic compounds. Three random splits into the sub-training, calibration, and test sets of the data for 3017 compounds were examined. The performance of the proposed approach is satisfactory. The average values of the statistical characteristics for external test set on three random splits are as follows: n=1173-1234, sensitivity = 0.8903±0.0390, specificity = 0.9869±0.0013, and accuracy = 0.9759±0.0043. Mechanistic interpretation of the suggested model is discussed.
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Cite this article as:
Toropov A.A., Toropova A.P., Benfenati E., Gini G., Leszczynska D. and Leszczynski J., CORAL: Classification Model for Predictions of Anti-Sarcoma Activity, Current Topics in Medicinal Chemistry 2012; 12 (24) . https://dx.doi.org/10.2174/1568026611212240004
DOI https://dx.doi.org/10.2174/1568026611212240004 |
Print ISSN 1568-0266 |
Publisher Name Bentham Science Publisher |
Online ISSN 1873-4294 |
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