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-one-out 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.
Keywords: 2-aminothiazole; QSAR; anti-prion; artificial neural network; multiple linear regression; support vector machine.