Revisiting a receptor-based pharmacophore hypothesis for human A(2A) adenosine receptor antagonists.

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.

A Novel Generalized 3D-QSAR Model of Camptothecin Analogs.

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.

Inhibitory effects of glycosaminoglycans on basal and stimulated transforming growth factor-β1 expression in mesangial cells: biochemical and structural considerations.

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.

Combining selectivity and affinity predictions using an integrated Support Vector Machine (SVM) approach: An alternative tool to discriminate between the human adenosine A(2A) and A(3) receptor pyrazolo-triazolo-pyrimidine antagonists binding sites.

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.

Prediction of the aqueous solvation free energy of organic compounds by using autocorrelation of molecular electrostatic potential surface properties combined with response surface analysis.

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.