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Virtual screening structure-based

Structure-based virtual screening is fundamental to the field of computer-aided drug design. It entails docking and scoring of libraries of small molecules to [Pg.297]


Table 10.4-5. Recent successes of structure-based virtual screening approaches. Table 10.4-5. Recent successes of structure-based virtual screening approaches.
Schulz-Gasch T, Stahl M. Binding site characteristics in structure-based virtual screening evaluation of current docking tools. J Mol Model 2003 9 47-57. [Pg.416]

Good A. Structure-based virtual screening protocols. Curr Opin Drug Discov Dev 2001 4 301-7. [Pg.417]

Lyne PD. Structure-based virtual screening an overview. Drug Discov Today 2002 7 1047-55. [Pg.417]

Filikov AV, Mohan V, Vickers TA, Griffey RH, Cook PD, Abagyan RA, James TL. Identification of ligands for RNA targets via structure-based virtual screening HIV-1 TAR. / Comput Aided Mol Design 2000 14 593-610. [Pg.423]

PF1BVS Pharmacophore-based virtual screening SBVS (Protein) Structure-based virtual screening... [Pg.86]

Molecular docking (structure-based virtual screening) on the basis of a target protein structure, ligand molecules that bind to the active site of the target are searched for. [Pg.61]

The use of structure-based virtual screening toward novel nuclear hormone receptor antagonists was described by Schapira et al. [263]. As only X-ray structures of ligand binding domains of agonist-bound nuclear receptors with exception of the antagonist-bound estrogen receptor-a are available, the authors constructed an inactive ... [Pg.93]

Fig. 4.6 Example for successful structure-based virtual screening to identify submicromolar tRNA-guanine transglycosylase (TGT) inhibitors based on X-ray structures ofweaker ligands. Fig. 4.6 Example for successful structure-based virtual screening to identify submicromolar tRNA-guanine transglycosylase (TGT) inhibitors based on X-ray structures ofweaker ligands.
Binding site characteristics in structure-based virtual screening evaluation of current docking tools. [Pg.104]

Fig. 6. Chemotype enrichment rates using a variety of structure-based virtual screening algorithms and constraint settings for CDK2. DOCK search incorporating target class critical pharmacophore constraints denoted by the mark. Adapted from ref. 70. Fig. 6. Chemotype enrichment rates using a variety of structure-based virtual screening algorithms and constraint settings for CDK2. DOCK search incorporating target class critical pharmacophore constraints denoted by the mark. Adapted from ref. 70.
E., O Neill, A., Hine, H., Burton, M. S., Voigt, J. H., Abagyan, R A., Bayne, M. L., Monsma, F. J., Jr. (2008) Discovery of novel chemotypes to a G-protein-coupled receptor through ligand-steered homology modeling and structure-based virtual screening. J Med Chem 51, 581-588. [Pg.171]

Brozic, P, Turk, S., Lanisnik Rizner, T., Gobec, S. (2009) Discovery of new inhibitors of aldo-keto reductase 1C1 by structure-based virtual screening. Mol Cell Endocrinol 301,245-250. [Pg.171]

Park, H., Bhattarai, B. R, Ham, S. W., Cho, H. (2009) Structure-based virtual screening approach to identify novel classes of PTP1B inhibitors. Eur JMed Chem 44, 3280-3284. [Pg.171]

Cavasotto, C. N., Orry, A. J. (2007) Ligand docking and structure-based virtual screening in drug discovery. Curr Top Med Chem 7,1006-1014. [Pg.172]

Waszkowycz, B. (2008) Towards improving compound selection in structure-based virtual screening. Drug Discov Today 13, 219-226. [Pg.173]


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