Receptor-based QSAR studies of non-peptide human oxytocin receptor antagonists

Jójárt, Balázs; Márki, Árpád

doi:10.1016/j.jmgm.2006.05.010

Cited by 10 publications

(1 citation statement)

References 34 publications

(32 reference statements)

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“…This indicated the predictive r 2 values for the RI CoMFA and CoMSIA of 0.734 and 0.800, respectively, while the corresponding predictive r 2 values for the RD CoMFA and CoM-SIA models were lower and amounted to 0.538 and 0.639, respectively [60]. Similar approaches were reported for the inhibitors of dipeptidyl peptidase IV [61], carbonic anhydrase II [62,63] and the non-peptide antagonists of human oxytocin receptor [64]. The same method was used to design novel azole antifungal compounds binding the lanosterol 14 -demethylase [65] and to investigate indirubin or N-phenyl-4-pyrazolo[1,5-b]pyridazin-3-ylpyrimidin-2-amine analogues active against glycogen synthase kinase 3 [66,67].…”

Section: Rd Comfasupporting

confidence: 66%

Receptor Dependent Multidimensional QSAR for Modeling Drug - Receptor Interactions

Polański

2009

CMC

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Quantitative Structure Activity Relationship (QSAR) is an approach of mapping chemical structure to properties. A significant development can be observed in the last two decades in this method which originated from the Hansch analysis based on the logP data and Hammett constant towards a growing importance of the molecular descriptors derived from 3D structure including conformational dynamics and solvation scenarios. However, molecular interactions in biological systems are complex phenomena generating extremely noisy data, if simulated in silico. This decides that activity modeling and predictions are a risky business. Molecular recognition uncertainty in traditional receptor independent (RI) m-QSAR cannot be eliminated but by the inclusion of the receptor data. Modeling ligand-receptor interactions is a complex computational problem. This has limited the development of the receptor dependent (RD) m-QSAR. However, a steady increase of computational power has also improved modeling ability in chemoinformatics and novel RD QSAR methods appeared. Following the RI m-QSAR terminology this is usually classified as RD 3/6D-QSAR. However, a clear systematic m-QSAR classification can be proposed, where dimension m refers to, the static ligand representation (3D), multiple ligand representation (4D), ligand-based virtual or pseudo receptor models (5D), multiple solvation scenarios (6D) and real receptor or target-based receptor model data (7D).

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Section: Rd Comfasupporting

confidence: 66%

Receptor Dependent Multidimensional QSAR for Modeling Drug - Receptor Interactions

Polański

2009

CMC

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Applying in silico tools to the discovery of novel CXCR4 inhibitors

Pérez‐Nueno

Ritchie

2010

Drug Development Research

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The process of HIV entry begins with the binding of the viral envelope glycoprotein gp120 to both the CD4 receptor and one of the CXCR4 or CCR5 chemokine co-receptors. There is currently considerable interest in developing novel ligands that can bind to these co-receptors and hence block virus-cell fusion. This article reviews the use of different in silico structure-based and ligand-based virtual screening (VS) tools for the discovery of potential HIV entry inhibitors for the CXCR4 receptor. More specifically, it discusses homology modelling, de novo design, docking, QSAR analyses, pharmacophore modelling, and similarity searches. Results from retrospective VS of a library of known CXCR4 inhibitors taken from the literature and from prospective VS of a combinatorial virtual library are reviewed. The structures of active compounds found by these approaches, as well as CXCR4 inhibitors currently in development, are also discussed. Drug Dev Res 72:95-111, 2011.

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