Bioactive conformations identification

Weide T, Modlinger A, Kessler H (2007) Spatial Screening for the Identification of the Bioactive Conformation of Integrin Ligands. 272 1-50 Weiss EA, Wasielewski MR, Ratner MA (2005) Molecules as Wires Molecule-Assisted Movement of Charge and Energy. 257 103-133... 

Spatial Screening for the Identification of the Bioactive Conformation of Integrin Ligands... 

IV. FLUORINE-CONTAINING TAXOIDS SAR STUDY AND FLUORINE PROBE APPROACH TO THE IDENTIFICATION OF BIOACTIVE CONFORMATIONS... 

D-QSAR. Since compounds are active in three dimensions and their shape and surface properties are major determinants of their activity, the attractiveness of 3D-QSAR methods is intuitively clear. Here conformations of active molecules must be generated and their features captured by use of conformation-dependent descriptors. Despite its conceptual attractiveness, 3D-QSAR faces two major challenges. First, since bioactive conformations are in many cases not known from experiment, they must be predicted. This is often done by systematic conformational analysis and identification of preferred low energy conformations, which presents one of the major uncertainties in 3D-QSAR analysis. In fact, to date there is no computational method available to reliably and routinely predict bioactive molecular conformations. Thus, conformational analysis often only generates a crude approximation of active conformations. In order to at least partly compensate for these difficulties, information from active sites in target proteins is taken into account, if available (receptor-dependent QSAR). Second, once conformations are modeled, they must be correctly aligned in three dimensions, which is another major source of errors in the system set-up for 3D-QSAR studies. 

Identification of Bioactive Conformations and Quest for a Pharmacophore for Paclitaxel... 

It is of particular interest that another 8 opioid selective nonpeptidic piperazine derivative agonist, SL-3111 (l-(4-/ert-butyl-3 -hydroxy)benzhydryl-4-benzylpiper-azine), was designed and synthesized based on the identification of the essential pharmacophores for peptide ligands to interact with 8 receptors, and on a model of the proposed bioactive conformation of the potent, conformationally constrained 8-opioid peptide ligand [(25,3/ )-TMT1]DPDPE [47]. This new series used the... 

Bersuker, I.B., Bahceci, S., Boggs, J.E. Improved electron-conformational method of pharmacophore identification and bioactivity prediction. Application to angiotensin converting enzyme inhibitors. Journal of Chemical Information and Computer Science 2000, 40, 1363-1376. 

Bersuker, l.B. (2003) Pharmacophore identification and quantitative bioactivity prediction using the electron-conformational method. Curr. Pharm. Design, 9, 1575-1606. 

Rosines, F., Bersuker, I.B. and Boggs, J.E. (2001) Pharmacophore identification and bioactivity prediction for group I metabotropic glutamate receptor agonists by the electron-conformational QSAR method. Quant. Struct. -Act. Rdat., 20, 327-334. 

Selection of an appropriate monomer is critical for producing bioactive polymers with targeted functions. The attributes of the monomer building block govern all aspects of bioactivity control over polymer length, polydispersity, conformational flexibility, ligand presentation, and degradability. Therefore, the identification of new substrates for ROMP has become an area of active research. 

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