CoMFA Study on Adenosine A2A Receptor Agonists

Doytchinova, Irini; Valkova, Iva; Natcheva, Roumiana

doi:10.1002/1521-3838(200107)20:2<124::aid-qsar124>3.0.co;2-v

Cited by 10 publications

(2 citation statements)

References 12 publications

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“…The first paper featuring the description of a CoMFA study applied to adenosine receptors was published by Jacobson and coworkers in 1995 and dealt with the 3D-QSAR of a series of N 6 -benzyladenosine-5'-uronamide analogs as agonists of the A 3 subtype [23]. Many similar studies followed in subsequent years, conducted by the same group as well as various other laboratories [24][25][26][27][28][29][30][31][32][33][34][35][36][37]. In this context, in 2007 Kim and Jacobson developed CoMFA and CoMSIA models based on structurally diverse adenosine analogues and successfully elucidated not only the molecular basis for their affinity but also those for their relative efficacy at the human A 3 receptor [35].…”

Section: Ligand-based 3d-qsarmentioning

confidence: 99%

Ligand and structure-based methodologies for the prediction of the activity of G protein-coupled receptor ligands

Costanzi

Tikhonova

Harden

2008

J Comput Aided Mol Des

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SummaryAccurate in silico models for the quantitative prediction of the activity of G protein-coupled receptor (GPCR) ligands would greatly facilitate the process of drug discovery and development. Several methodologies have been developed based on the properties of the ligands, the direct study of the receptor-ligand interactions, or a combination of both approaches. Ligand-based three-dimensional quantitative structure-activity relationships (3D-QSAR) techniques, not requiring knowledge of the receptor structure, have been historically the first to be applied to the prediction of the activity of GPCR ligands. They are generally endowed with robustness and good ranking ability; however they are highly dependent on training sets. Structure-based techniques generally do not provide the level of accuracy necessary to yield meaningful rankings when applied to GPCR homology models. However, they are essentially independent from training sets and have a sufficient level of accuracy to allow an effective discrimination between binders and nonbinders, thus qualifying as viable lead discovery tools. The combination of ligand and structure-based methodologies in the form of receptor-based 3D-QSAR and ligand and structure-based consensus models results in robust and accurate quantitative predictions. The contribution of the structure-based component to these combined approaches is expected to become more substantial and effective in the future, as more sophisticated scoring functions are developed and more detailed structural information on GPCRs is gathered.

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Section: Ligand-based 3d-qsarmentioning

confidence: 99%

Ligand and structure-based methodologies for the prediction of the activity of G protein-coupled receptor ligands

Costanzi

Tikhonova

Harden

2008

J Comput Aided Mol Des

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“…In this sense, Doytchinova et al [79] applied a step-wise CoMFA-based procedure to a series of 51 derivatives of C2oxyadenosine, which were previously described as A 2A AR agonists [80], in order to select the most predictive conformation for binding to the A 2A adenosine receptor.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Structure Activity Relationships as Useful Tools for the Design of New Adenosine Receptor Ligands. 1. Agonist

Helguera

Teijeira²,

Terán³

et al. 2006

CMC

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In order to minimize expensive drug failures it is essential to determine the potential biological activity of new candidates as early as possible. In view of the large libraries of nucleoside analogues that are now being handled in organic synthesis, the identification of a drugs biological activity is advisable even before synthesis and this can be achieved using predictive biological activity methods. In this sense, computer aided rational drug design strategies like Quantitative Structure Activity Relationships (QSAR) or docking approaches have emerged as promising tools. Although a large number of in silico approaches have been described in the literature for the prediction of different biological activities, the use of traditional QSAR applications in the development of new agonist molecules with affinity toward adenosine receptors is scarce. This review attempts to summarize the current level of knowledge concerning computational affinity predictions for adenosine receptors using QSAR models based on knowledge of the agonist ligands. Several computational protocols and different 2D and 3D descriptors have been described in the literature for these targets, but more effort is still required in this area.

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2009

Molecular Descriptors for Chemoinformatics

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CoMFA Study on Adenosine A2A Receptor Agonists

Cited by 10 publications

References 12 publications

Ligand and structure-based methodologies for the prediction of the activity of G protein-coupled receptor ligands

Ligand and structure-based methodologies for the prediction of the activity of G protein-coupled receptor ligands

Quantitative Structure Activity Relationships as Useful Tools for the Design of New Adenosine Receptor Ligands. 1. Agonist

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