A novel quantitative structure-activity relationship method to predict the affinities of MT3 melatonin binding site

The linear regression (LR) and non-linear regression methods--grid search-support vector machine (GS-SVM) and projection pursuit regression (PPR) were used to develop quantitative structure-activity relationship (QSAR) models for a series of derivatives of naphthalene, benzofurane and indole with respect to their affinities to MT3/quinone reductase 2 (QR2) melatonin binding site. Five molecular descriptors selected by genetic algorithm (GA) were used as the input variables for the LR model and two non-linear regression approaches. Comparison of the results of the three methods indicated that PPR was the most accurate approach in predicting the affinities of the MT3/QR2 melatonin binding site. This confirmed the capability of PPR for the prediction of the binding affinities of compounds. Moreover, it should facilitate the design and development of new selective MT3/QR2 ligands.