Combining selectivity and affinity predictions using an integrated Support Vector Machine (SVM) approach: An...

G Protein-coupled receptors (GPCRs) selectivity is an important aspect of drug discovery process, and distinguishing between related receptor subtypes is often the key to therapeutic success. Nowadays, very few valuable computational tools are available for the prediction of receptor subtypes selectivity. In the present study, we present an alternative application of the Support Vector Machine (SVM) and Support Vector Regression (SVR) methodologies to simultaneously describe both A(2A)R versus A(3)R subtypes selectivity profile and the corresponding receptor binding affinities. We have implemented an integrated application of SVM-SVR approach, based on the use of our recently reported autocorrelated molecular descriptors encoding for the Molecular Electrostatic Potential (autoMEP), to simultaneously discriminate A(2A)R versus A(3)R antagonists and to predict their binding affinity to the corresponding receptor subtype of a large dataset of known pyrazolo-triazolo-pyrimidine analogs. To validate our approach, we have synthetized 51 new pyrazolo-triazolo-pyrimidine derivatives anticipating both A(2A)R/A(3)R subtypes selectivity and receptor binding affinity profiles.