Authors: Isarankura-Na-Ayudhya, Chartchalerm
Mandi, Prasit
Nantasenamat, Chanin
Prachayasittikul, Virapong
Srungboonmee, Kakanand
Title: QSAR study of anti-prion activity of 2-aminothiazoles
Language (ISO): en
Abstract: 2-aminothiazoles is a class of compounds capable of treating life-threatening prion diseases. QSAR studies on a set of forty-seven 2-aminothiazole derivatives possessing anti-prion activity were performed using multivariate analysis, which comprised of multiple linear regression (MLR), artificial neural network (ANN) and support vector machine (SVM). The results indicated that MLR afforded reasonable performance with a correlation coefficient (r) and root mean squared error (RMSE) of 0.9073 and 0.2977, respectively, as obtained from leave-oneout cross-validation (LOO-CV). More sophisticated learning methods such as SVM provided models with the highest accuracy with r and RMSE of 0.9471 and 0.2264, respectively, while ANN gave reasonable performance with r and RMSE of 0.9023 and 0.3043, respectively, as obtained LOO-CV calculations. Descriptor analysis from the regression coefficients of the MLR model suggested that compounds should be asymmetrical molecule with low propensity to form hydrogen bonds and high frequency of N content at topological distance 02 in order to provide good activities. Insights from QSAR studies is anticipated to be useful in the design of novel derivatives based on the 2-aminothiazole scaffold as potent therapeutic agents against prion diseases.
Subject Headings: 2-aminothiazole
anti-prion
artificial neural network
multiple linear regression
QSAR
support vector machine
URI: http://hdl.handle.net/2003/29762
http://dx.doi.org/10.17877/DE290R-5139
Issue Date: 2012-11-15
Appears in Collections:Original Articles

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