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The application of both structure- and ligand-based design approaches represents to date one of the most useful strategies in the discovery of new drug candidates. In the present paper, we investigated how the application of docking-driven conformational analysis can improve the predictive ability of 3D-QSAR statistical models. With the use of the crystallographic structure in complex with the high affinity antagonist ZM 241385 (4-(2-[7-amino-2-(2-furyl)[1,2,4]-triazolo[2,3-a][1,3,5]triazin-5-ylamino]ethyl)phenol), we revisited a general pharmacophore hypothesis for the human A(2A) adenosine receptor of a set of 751 known antagonists, by applying an integrated ligand- and structure-based approach. Our novel pharmacophore hypothesis has been validated by using an external test set of 29 newly synthesized human adenosine receptor antagonists.

In the present paper, we are interested to explore if the application of docking-driven conformational analysis could increase the goodness of 3D-QSAR statistical models, as alternative approach to a conventional ligand-based conformer generation. In particular, we have selected as peculiar key-study an ensemble of Camptothecin (CPT) analogs classified as human DNA Topoisomerase I (Top1) selective inhibitors. The CPT analogs dataset has been recently analyzed by Hansch and Verma using a classical 2D-QSAR study., (Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

A number of glycosaminoglycan (GAG) species related to heparin, dermatan sulfate (DeS) and chondroitin sulfate were tested for their ability to interfere with the physiological expression and/or pathological overexpression of the TGF-β1 gene. The influence of the molecular weight, molecular weight distribution, degree of sulfation and location of the sulfate groups was examined in an attempt to unveil fine relationships between structure and activity. The nature of the polysaccharide plays a major part, heparins proving able to inhibit both basal and stimulated TGF-β1 gene expression, DeSs being essentially inactive and chondroitin sulfates only inhibiting stimulated TGF-β1 gene expression. Within this frame, the particular physical and chemical properties of some GAGs appear to further modulate TGF-β1 gene response. Judging from our investigation, chondroitin sulfates seem the most promising for potential pharmacological applications in disorders characterized by fibrogenic TGF-β1 overexpression.

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.

Several quantitative structure-property relationship (QSPR) approaches have been explored for the prediction of aqueous solubility or aqueous solvation free energies, DeltaG(sol), as crucial parameter affecting the pharmacokinetic profile and toxicity of chemical compounds. It is mostly accepted that aqueous solvation free energies can be expressed quantitatively in terms of properties of the molecular surface electrostatic potentials of the solutes. In the present study we have introduced autocorrelation molecular electrostatic potential (autoMEP) vectors in combination with nonlinear response surface analysis (RSA) as alternative 3D-QSPR strategy to evaluate the aqueous solvation free energy of organic compounds. A robust QSPR model (r(cv)=0.93) has been obtained by using a collection of 248 organic chemicals. An external test set based on 23 molecules confirmed the good predictivity of the autoMEP/RSA model suggesting its further applicability in the in silico prediction of water solubility of large organic compound libraries.

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.

In the last 5 years, many efforts have been conducted searching potent and selective human A(3) adenosine antagonists. In this field several different classes of compounds, possessing very good affinity (nM range) and with a broad range of selectivity, have been proposed. Recently, our group synthesized a new series of pyrazolo-triazolo-pyrimidines bearing different substitutions at the N(5) and N(8) positions, which have been described as highly potent and selective human A(3) adenosine receptor antagonists. The present review summarizes available data and provides an overview of the structure-activity relationships found for this class of human A(3) adenosine receptor antagonists.

The aim of virtual high-throughput screening is the identification of biologically relevant molecules among either tangible or virtual (large) collections of compounds. Likewise, high-throughput screening (HTS) and high-throughput virtual screening (HTVS) methods are becoming very important within the drug discovery process. HTVS methods can be categorised as either 'ligand-based' or 'structure-based' depending on if a direct knowledge of the three-dimensional target structure is required. A summary of the most promising computational approaches is reviewed. Advantages and shortcomings of the methodology are also discussed.

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.

We have recently reported that the combination of molecular electrostatic potential (MEP) surface properties (autocorrelation vectors) with the conventional partial least squares (PLS) analysis can be used to produce a robust ligand-based 3D structure-activity relationship (autoMEP/PLS) for the prediction of the human A3 receptor antagonist activities. Here, we present the application of the 3D-QSAR (autoMEP/PLS) approach as an efficient and alternative pharmacodynamic filtering method for small-sized virtual library. For this purpose, a small-sized combinatorial library (841 compounds) was derived from the scaffold of the known human A3 antagonist pyrazolo-triazolo-pyrimidines. The most interesting analogues were further prioritized for synthesis and pharmacological characterization. Remarkably, we have found that all the newly synthetized compounds are correctly predicted as potent human A3 antagonists. In particular, two of them are correctly predicted as sub-nanomolar inhibitors of the human A3 receptor.

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.

There are pharmacological evidences that A(2B) receptors are involved in inflammatory processes, such as asthma. For this reason, many efforts has been made for identifying selective A(2B) antagonists as anti-asthmatic agents. The updated material related to this field has been rationalised and arranged in order to offer an overview of the topic.

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.