Pharmacophore Modeling, Virtual Screening, and <i>in Vitro</i> Testing Reveal Haloperidol, Eprazinone, and Fenbutrazate as Neurokinin Receptors Ligands

Krautscheid, Y; Senning, Carl Johann Åke; Sartori, Simone; Singewald, Nicolas; Schuster, Daniela; Stuppner, Hermann

doi:10.1021/ci500106z

Cited by 13 publications

(9 citation statements)

References 59 publications

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“…Based on X-ray crystal structures of small molecule inhibitors in complex with sEH and structural information on sEH inhibitors from literature, several pharmacophore models were generated. The first aim of the pharmacophore model generation process was to create a model set, which finds as many active molecules in a data set containing published sEH inhibitors and as few confirmed inactive compounds from the literature as possible. , To accomplish this aim, preferentially structure-based models were used, but for complementation ligand-based models were also employed. For an optimal coverage of the active chemical space of the inhibitors, two pharmacophore modeling programs (LigandScout (LS) version 3.03 and Discovery Studio (DS) version 3.0) based on different screening algorithms were used .…”

Section: Resultsmentioning

confidence: 99%

Discovery of Potent Soluble Epoxide Hydrolase (sEH) Inhibitors by Pharmacophore-Based Virtual Screening

Waltenberger

Garscha

Temml

et al. 2016

J. Chem. Inf. Model.

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There is an increasing interest in the development of soluble epoxide hydrolase (sEH) inhibitors, which block the degradation of endogenous anti-inflammatory epoxyeicosatrienoic acids. Within this study, a set of pharmacophore models for sEH inhibitors was developed. The Specs database was virtually screened and a cell-free sEH activity assay was used for the biological investigation of virtual hits. In total, out of 48 tested compounds, 19 were sEH inhibitors with IC50 < 10 μM, representing a prospective true positive hit rate of 40%. Six of these compounds displayed IC50 values in the low nanomolar range. The most potent compound 21, a urea derivative, inhibited sEH with an IC50 = 4.2 nM. The applied approach also enabled the identification of diverse chemical scaffolds, e.g. the pyrimidinone derivative 29 (IC50 = 277 nM). The generated pharmacophore model set therefore represents a valuable tool for the selection of compounds for biological testing.

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Section: Resultsmentioning

confidence: 99%

Discovery of Potent Soluble Epoxide Hydrolase (sEH) Inhibitors by Pharmacophore-Based Virtual Screening

Waltenberger

Garscha

Temml

et al. 2016

J. Chem. Inf. Model.

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“…Virtual screening is often deployed in these projects to prioritize molecules for testing and minimizing the number of compounds to be investigated in biological screens. The ultimate aim is the identification of novel lead compounds for a specific disease-related target, which can be developed into drug candidates for the treatment of the intended disease, with numerous studies during the last years describing such applications [39,40,41,42,43,44]. For example, Ha et al reported the discovery of novel ligands for the chemokine receptor CXCR2 by using a ligand-based pharmacophore modeling approach [45].…”

Section: Applications Of Pharmacophore-based Vsmentioning

confidence: 99%

Pharmacophore Models and Pharmacophore-Based Virtual Screening: Concepts and Applications Exemplified on Hydroxysteroid Dehydrogenases

et al. 2015

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Abstract:Computational methods are well-established tools in the drug discovery process and can be employed for a variety of tasks. Common applications include lead identification and scaffold hopping, as well as lead optimization by structure-activity relationship analysis and selectivity profiling. In addition, compound-target interactions associated with potentially harmful effects can be identified and investigated. This review focuses on pharmacophore-based virtual screening campaigns specifically addressing the target class of hydroxysteroid dehydrogenases. Many members of this enzyme family are associated with specific pathological conditions, and pharmacological modulation of their activity may represent promising therapeutic strategies. On the other hand, unintended interference with their biological functions, e.g., upon inhibition by xenobiotics, can disrupt steroid hormone-mediated effects, thereby contributing to the development and progression of major diseases. Besides a general introduction to pharmacophore modeling and pharmacophore-based virtual screening, exemplary case studies from the field of short-chain dehydrogenase/reductase (SDR) research are presented. These success stories highlight the suitability of pharmacophore modeling for the various application fields and suggest its application also in futures studies.

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“…Pharmacophore models have proved to be useful for the selection of focused sets of compounds [1,2,3,4]. There are two kinds of pharmacophores: (i) structure-based pharmacophores derived directly from X-ray structures of protein-ligand complexes, and (ii) ligand-based pharmacophores derived from structures of known active compounds.…”

Section: Introductionmentioning

confidence: 99%

Ligand-Based Pharmacophore Modeling Using Novel 3D Pharmacophore Signatures

et al. 2018

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Pharmacophore modeling is a widely used strategy for finding new hit molecules. Since not all protein targets have available 3D structures, ligand-based approaches are still useful. Currently, there are just a few free ligand-based pharmacophore modeling tools, and these have a lot of restrictions, e.g., using a template molecule for alignment. We developed a new approach to 3D pharmacophore representation and matching which does not require pharmacophore alignment. This representation can be used to quickly find identical pharmacophores in a given set. Based on this representation, a 3D pharmacophore ligand-based modeling approach to search for pharmacophores which preferably match active compounds and do not match inactive ones was developed. The approach searches for 3D pharmacophore models starting from 2D structures of available active and inactive compounds. The implemented approach was successfully applied for several retrospective studies. The results were compared to a 2D similarity search, demonstrating some of the advantages of the developed 3D pharmacophore models. Also, the generated 3D pharmacophore models were able to match the 3D poses of known ligands from their protein-ligand complexes, confirming the validity of the models. The developed approach is available as an open-source software tool: and .

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Pharmacophore Modeling, Virtual Screening, and in Vitro Testing Reveal Haloperidol, Eprazinone, and Fenbutrazate as Neurokinin Receptors Ligands

Cited by 13 publications

References 59 publications

Discovery of Potent Soluble Epoxide Hydrolase (sEH) Inhibitors by Pharmacophore-Based Virtual Screening

Discovery of Potent Soluble Epoxide Hydrolase (sEH) Inhibitors by Pharmacophore-Based Virtual Screening

Pharmacophore Models and Pharmacophore-Based Virtual Screening: Concepts and Applications Exemplified on Hydroxysteroid Dehydrogenases

Ligand-Based Pharmacophore Modeling Using Novel 3D Pharmacophore Signatures

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