G protein-coupled receptors (GPCRs) represent the largest family known of signal-transducing molecules. They convey signals for light and many extracellular regulatory molecules. GPCRs have been found to be dysfunctional/dysregulated in a growing number of human diseases and they have been estimated to be the targets of more than 40% of the drugs used in clinical medicine today. The crystal structure of rhodopsin provides the first three-dimensional GPCR information, which now supports homology modeling studies and structure-based drug design approaches. Here, we review our recent work on adenosine receptors, a family of GPCRs and, in particular, on A(3) adenosine receptor subtype antagonists. We will focus on an alternative approach to computationally explore the multi-conformational space of the antagonist-like state of the human A(3) receptor. We define ligand-based homology modeling as new approach to simulate the reorganization of the receptor induced by the ligand binding. The success of this approach is due to the synergic interaction between theory and experiment.

A database of 106 human A3 adenosine receptor antagonists was used to derive two alternative PLS models: one starting from CoMFA descriptors and the other starting from the autocorrelation descriptors. The peculiarity of this work is the introduction of autocorrelation vectors as molecular descriptors for the PLS analysis. The autocorrelation allows comparing molecules (and their properties) with different structures and with different spatial orientation without any previous alignment. In particular, Molecular Electrostatic Potential (MEP) was the property computed and its information encoded in autocorrelation vectors. The 3D spatial distribution and the values of the electrostatic potential is in fact largely responsible for the binding of a substrate to its receptor binding site. Validation was done with an external test set and the results of the two models were compared. Interestingly, our preliminary results seem to indicate that this new alternative approach could robustly compete with the already well consolidated CoMFA approach. In particular, we have suggested that it could be a very interesting tool to filter large structural database in several virtual screening applications.

The combination of molecular electrostatic potential (MEP) surface properties (autocorrelation vectors) with the conventional partial least squares (PLS) analysis has been used for the prediction of the human A(3) receptor antagonist activities. Three-hundred-fifty-eight structurally diverse human A(3) receptor antagonists have been utilized to generate a novel ligand-based three-dimensional structure-activity relationship. Remarkably, our chemical library includes all 21 important chemical classes of human A(3) antagonists currently discovered, and it represents the largest molecular collection used to generate a general human A(3) antagonist structure-activity relationship. A robust quantitative model has been obtained as described by both cross-validated correlation coefficient (r(cv) = 0.81) and prediction capability (r(pred) = 0.82). The proposed MEP/PLS approach can be considered as an alternative hit identification tool in virtual screening applications.

A computer-aided approach has been developed in order to understand the molecular pharmacology of human A3R, and specifically, to lead to the discovery and structural refinement of new, potent and selective human A3R antagonists. This review focuses on our combined target-based and ligand-based drug design strategy, recently applied to provide more accurate information about the recognition mode on human A3R of some pyrazolotriazolopyrimidine and triazoloquinoxalinone analogs. The 3D rhodopsin-based homology model of human A3R has represented the starting point of our approach. A high throughput molecular docking method on the considered antagonists has allowed us to generate a receptor-based pharmacophore model. A novel "Y-shaped" pharmacophore binding motif has been proposed for both pyrazolotriazolopyrimidine and triazoloquinoxalinone derivatives. Moreover, related receptor-based 3D-QSAR analysis has been carried out to provide a suitable tool for prediction of the antagonists binding affinity on human A3R.

Ligand-based drug design represents an important research field in the drug discovery and optimisation process. This review provides an overview about the theoretical background of the quantitative structure activity relationship (QSAR) models.