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Pharmacophore Detection and Searching

Graphs of query and test molecules can be compared by graph matching (subgraph detection) algorithms or systematic comparison of inter-feature distances. Two molecules are considered similar if their pharmacophores match for at least one predicted conformation. In order to explore conformational space and generate conformational ensembles, multiple compound conformations are typically generated by systematic conformational search (in increments) around rotatable bonds. [Pg.20]

Some hits also revealed sufficient selectivity of type 1 inhibition versus the type 2 isoform, which is advantageous for the side-effect profile of these compounds. Comparison of the model for llp-HSDl inhibitors with the X-ray crystal structure (which was published shortly after model generation and VS) showed good correlation of the chemical features responsible for ligand binding. In another study, a combination of common feature-based qualitative and quantitative models was used as 3D pharmacophore search query to successfully detect novel endothelin-A antagonistic lead structures. [Pg.100]

Figure 3 Structural alignments with discrete properties. Methods are based on discrete properties using the DG algorithm (1) or clique-detection (11) as implemented in distance comparisons (DISCO), and Apex-3D. The structure representation, based on discrete properties, resorts to one atomic descriptor (I), usually the atom type, or multiple atomic or site descriptors (II). In the first method (I), the conformational analysis is restricted to the generation of molecular geometries which allow a common arrangement of selected phaimacophoric moieties present in a rigid compound used as template. In the second method (II), the conformational analysis procedure may involve a systematic enumeration of all the possible conformadons for each ligand. The search similarity is directed towards the confirmation of a predefined pharmacophore postulated by the modeler or from some classical SAR in the case of the active analog approach (1), or the automated identification of pharmacophores and bioacdve conformations (II)... Figure 3 Structural alignments with discrete properties. Methods are based on discrete properties using the DG algorithm (1) or clique-detection (11) as implemented in distance comparisons (DISCO), and Apex-3D. The structure representation, based on discrete properties, resorts to one atomic descriptor (I), usually the atom type, or multiple atomic or site descriptors (II). In the first method (I), the conformational analysis is restricted to the generation of molecular geometries which allow a common arrangement of selected phaimacophoric moieties present in a rigid compound used as template. In the second method (II), the conformational analysis procedure may involve a systematic enumeration of all the possible conformadons for each ligand. The search similarity is directed towards the confirmation of a predefined pharmacophore postulated by the modeler or from some classical SAR in the case of the active analog approach (1), or the automated identification of pharmacophores and bioacdve conformations (II)...

See other pages where Pharmacophore Detection and Searching is mentioned: [Pg.74]    [Pg.75]    [Pg.77]    [Pg.79]    [Pg.81]    [Pg.83]    [Pg.85]    [Pg.87]    [Pg.74]    [Pg.75]    [Pg.77]    [Pg.79]    [Pg.81]    [Pg.83]    [Pg.85]    [Pg.87]    [Pg.52]    [Pg.109]    [Pg.683]    [Pg.310]    [Pg.356]    [Pg.135]    [Pg.136]    [Pg.41]    [Pg.197]    [Pg.87]    [Pg.321]    [Pg.575]    [Pg.482]    [Pg.485]    [Pg.667]    [Pg.21]    [Pg.118]    [Pg.340]    [Pg.161]    [Pg.119]    [Pg.44]    [Pg.548]   


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