Pharmacophores virtual screening

NMR 3D structure of the undecapeptide U-II generated a ligand pharmacophore hypothesis that served as query for the virtual screening of the Aventis in-house compound repository. Active leads from six different chemical classes could be identihed by the 3D search, for example, compound 17 (ECso = 400nM Fig. 16.2) [91]. 

Flohr S, Kurz M, Kostenis E, Brkovich A, Fournier A, Klabunde T. Identification of nonpeptidic urotensin II receptor antagonists by virtual screening based on a pharmacophore model derived from structure-activity relationships and nuclear magnetic resonance studies on urotensin II. J Med Ghent 2002 45 1799-805. 

Brenk R, Meyer EA, Reuter K, Stubbs MT, Garcia GA, Diederich E, Klebe G. Crystallographic study of inhibitors of tRNA-guanine transglycosylase snggests a new structure-based pharmacophore for virtual screening. J Mol Biol 2004 338 55-75. 

Wolber G, Langer T LigandScout 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. / Chem Inf Model 2005 45 160-9. 

Keywords VS, Virtual Screening, Lead discovery, lead, HTS, Pharmacophore-Based, Structure-Based, Fragment-based, Ligand-based, Docking, Scoring, hybrid workflows, VS strategy, Benchmarking VS... 

PF1BVS Pharmacophore-based virtual screening SBVS (Protein) Structure-based virtual screening... 

A virtual screening on pharmacophores derived from available structures of FabH delivered YKAs3003 (10). This Schiff base displays a KV) of 20 nM against E. coli FabH but has no significant antibacterial activity [19]. Other derivatives based on similar templates have been subsequently reported, such as 11 [20] and 12 [21]. They show low micromolar IC50 s against E. coli FabH with broad-spectrum antibacterial activity... 

Another group has evaluated self-organizing maps [63] and shape/ pharmacophore models [64]. They developed a new method termed SQUIRREL to compare molecules in terms of both shape and pharmacophore points. Thus from a commercial library of 199,272 compounds, 1926 were selected based on self-organizing maps trained on peroxisome proliferator-activated receptor a (PPARa) "activity islands." The compounds were further evaluated with SQUIRREL and 7 out of 21 molecules selected were found to be active in PPARa. Furthermore, a new virtual screening technique (PhAST) was developed based on representation of molecules as text strings that describe their pharmacophores [65]. 

Validation of Antitarget Pharmacophore Models 6.3.3.1 Virtual Screening Hit Rates and Yields... 

Table 6.3 Hit rate and yield from virtual screen of MDDR test set database using both a- A pharmacophores as selection filter [16].

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