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.

Pyrazolo-triazolo-pyrimidines as adenosine receptor antagonists: A complete structure-activity profile.

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.

Linear and nonlinear 3D-QSAR approaches in tandem with ligand-based homology modeling as a computational strategy to depict the pyrazolo-triazolo-pyrimidine antagonists binding site of the human adenosine A2A receptor.

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.

Ligand-based drug design methodologies in drug discovery process: an overview.

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.

Combining ligand-based and structure-based drug design in the virtual screening arena.

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.

Ligand-based homology modeling as attractive tool to inspect GPCR structural plasticity.

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 application of a 3D-QSAR (autoMEP/PLS) approach as an efficient pharmacodynamic-driven filtering method for small-sized virtual library: application to a lead optimization of a human A3 adenosine receptor antagonist.

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.

Autocorrelation of molecular electrostatic potential surface properties combined with partial least squares analysis as alternative attractive tool to generate ligand-based 3D-QSARs.

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.

A2B adenosine receptor antagonists: recent developments.

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.

Autocorrelation of molecular electrostatic potential surface properties combined with partial least squares analysis as new strategy for the prediction of the activity of human A(3) adenosine receptor antagonists.

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.