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Pharmacophore-based ligand

Ligand-based pharmacophore Pharmacophore hypotheses generated from known inhibitors... [Pg.32]

Keywords VS, Virtual Screening, Lead discovery, lead, HTS, Pharmacophore-Based, Structure-Based, Fragment-based, Ligand-based, Docking, Scoring, hybrid workflows, VS strategy, Benchmarking VS... [Pg.85]

In this section, a number of case studies (Table 5) in which different types of VS methods are combined into a hybrid workflow. Often these combine a fast, ligand or pharmacophore-based method with a later docking method. The latter is useful at the inspection stage as it allows the molecule to be reviewed within the context of the protein binding site. A poor binding pose can be an indicator of a poor fit. Furthermore, possible interactions outside the scope of the molecules used to train the ligand-based method can be identified. [Pg.109]

Ligand-Based and Receptor-Based Pharmacophore Modeling and QSAR Analysis... [Pg.158]

A great variety of methods and techniques are now available for pharmacophore model generation. These methods are based on the appropriate choice of the ligand set (ligand-based, when much information is available on different ligand classes), and/or on one or more target/antitarget proteins (structure-based, particularly useful when... [Pg.183]

Chemical Information, Irvine CA Tripos, Inc. St. Louis MO), similarity searching can be carried out around a well-defined compound class using local descriptors such as atom pairs [46, 47] or topomeric shape [48, 49]. Also, ligand-based pharmacophore searches are able to identify follow-up compounds that are less obvious and more diverse than similarity searches [30, 50-54]. The problem with the latter methods is defining the molecular shape or pharmacophore specifically enough to be useful when there are few hits within a compound class and they cannot be reliably aligned (as is often the case for NMR hits in the absence of detailed structural information). [Pg.399]

Fig. 4.1 Virtual screening tools can be categorized by the compound data to be screened (compound collection, combinatorial library, chemistry space) and the query type (structure-based, ligand-based, descriptor-based, pharmacophore-based). The output is always a list of compounds together with a score quantifying the fit to the query. Fig. 4.1 Virtual screening tools can be categorized by the compound data to be screened (compound collection, combinatorial library, chemistry space) and the query type (structure-based, ligand-based, descriptor-based, pharmacophore-based). The output is always a list of compounds together with a score quantifying the fit to the query.
Pharmacophore mapping (pharmacophore-based virtual screening) given the relative orientation of a few functional groups in space (a pharmacophore), ligand molecules that are able to fulfill the pharmacophore are searched for. [Pg.62]

Another retrospective analysis of already known H DAC inhibitors was carried out by You et al. [68]. They generated a 3D chemical-feature-based pharmacophore model and compared the ligand-based model with the structural-functional requirements for the binding of the HDAC inhibitors. Using this model, the interactions between the benzamide MS-275 and HDAC were explored. The result showed that the type and spatial location of chemical features encoded in the pharmacophore are in full agreement with the enzyme-inhibitor interaction pattern identified from molecular docking. However, also in this study no experimental validation of the modeling results was provided. [Pg.66]

Recent examples of successful peptide-ligand based discoveries of drug-like peptidomimetics include the discovery of SST antagonists, or the discovery of non-peptidic antagonists of the recently deorphanized urotensin II receptor at Sanofi-Aventis. ° As illustrated in Fig. 3, Flohr etal. used 3D models of the NMR solution structure of cyclic peptide derivatives of Urotensin II as a template for virtual 3D pharmacophore searches which resulted into non-peptidic candidates for lead optimization. [Pg.13]


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