1,720,968 research outputs found
Combining ligand-based and structure-based drug design in the virtual screening arena
No full textThe 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 homology modeling as attractive tool to inspect GPCR structural plasticity
No full textG 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
Novel strategies for the design of new potent and selective human A3 receptor antagonists: an update
No full textA 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
Prediction of the acqueous solvation free energy of organic compounds by using autocorrelation of molecular electrostatic potential surface properties combined with response surface analysis
No full textSeveral 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
Response Surface Analysis as aternative 3D-QSAR tool: human A3 adenosine receptor antagonists as a key study.
No full textG protein-coupled receptors as challenging druggable targets: insights from in silico studies
No full textA Novel Generalized 3D-QSAR Model of Camptothecin Analogs
No full textIn 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
Autocorrelation of Molecular Electrostatic Potential Surface Properties Combined with Partial Least Squares Analysis as New Strategy for the Prediction of the Activity of Human A3 Adenosine Receptor Antagonists.
No full textThe 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
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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