Predictive QSAR models of 3-acylamino-2-aminopropionic acid derivatives as partial agonists of the glycine site on the NMDA receptor

Abstract

A quantitative structure–activity relationship (QSAR) model has been carried out for a series of 3-acylamino-2-aminopropionic acid derivatives with high affinities in a binding assay for the glycine site. The replacement method (RM) and stepwise-multiple linear regression (Stepwise-MLR) strategies are used as feature selection (descriptor selection). The model of the relationship between selected molecular descriptors and pK i data has been achieved by linear (multiple linear regression, MLR) and nonlinear (support vector machine, SVM) methods. Leave-one-out cross-validation and external-validation were carried out with the aim for evaluating the predictive capability of the models. The squared correlation coefficients of experimental versus predicted activities for the test set obtained by MLR and SVM models using RM feature selection are 0.463 and 0.655, respectively. Our best QSAR model illustrates the importance of an adequate distribution of atomic properties represented in 3D frames and reveals the mass and van der Waals volumes as the most influencing atomic properties in the structures of the 3-acylamino-2-aminopropionic acid derivatives.