Using multivariate adaptive regression splines to QSAR studies of dihydroartemisinin derivatives.

QSAR models for analogs of antiplasmodial artemisinin compounds were established, based on atomic net charges by using multivariate adaptive regression splines (MARS) in comparison with some other methods such as multiple linear regression, alternating conditional expectations and projection pursuit regression. The established models were then evaluated by an Anova decomposition procedure so that the effects of each predictor (additive or interaction) could be viewed graphically, facilitating the interpretation of the underlying relationship. It was found that the QSARs derived from the MARS method are the most satisfactory predictive models, and that the artemisinin pharmacophore identification is in agreement with previous experimental findings.