Abstract
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.
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