Abstract
The integration of ligand- and structure-based strategies might sensitively
increase the success of drug discovery process. We have recently
described the application of Molecular Electrostatic Potential autocorrelated
vectors (autoMEPs) in generating both linear (Partial Least-Square,
PLS) and nonlinear (Response Surface Analysis, RSA) 3D-QSAR models
to quantitatively predict the binding affinity of human adenosine
A3 receptor antagonists. Moreover, we have also reported a novel
GPCR modeling approach, called Ligand-Based Homology Modeling (LBHM),
as a tool to simulate the conformational changes of the receptor
induced by ligand binding. In the present study, the application
of both linear and nonlinear 3D-QSAR methods and LBHM computational
techniques has been used to depict the hypothetical antagonist binding
site of the human adenosine A2A receptor. In particular, a collection
of 127 known human A2A antagonists has been utilized to derive two
3D-QSAR models (autoMEPs/PLS&RSA). In parallel, using a rhodopsin-driven
homology modeling approach, we have built a model of the human adenosine
A2A receptor. Finally, 3D-QSAR and LBHM strategies have been utilized
to predict the binding affinity of five new human A2A pyrazolo-triazolo-pyrimidine
antagonists finding a good agreement between the theoretical and
the experimental predictions.
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