Evidence map›Paper›PMID 33810008›Full record

ReviewMolecules (Basel, Switzerland)2021

Molecular Modeling of Histamine Receptors-Recent Advances in Drug Discovery.

Pakhuri Mehta, Przemysław Miszta, Sławomir Filipek

Abstract readReview
In one paragraph

Review in Molecules (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Substituted Purines as High-Affinity Histamine HPharmaceuticals (Basel, Switzerland) · 2022
    Article
  9. Article
  10. Why Monoamine Oxidase B Preferably MetabolizesInternational journal of molecular sciences · 2022
    Article
  11. Article
  12. Article
  13. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Pakhuri MehtaBiological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, 02-093 Warsaw, Poland.
Przemysław MisztaBiological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, 02-093 Warsaw, Poland.ORCID 0000-0003-2317-273X
Sławomir FilipekBiological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, 02-093 Warsaw, Poland.ORCID 0000-0003-3147-3858

Funding

Narodowym Centrum Nauki 2017/25/B/NZ7/02788
6 · The paper itself

Abstract

The recent developments of fast reliable docking, virtual screening and other algorithms gave rise to discovery of many novel ligands of histamine receptors that could be used for treatment of allergic inflammatory disorders, central nervous system pathologies, pain, cancer and obesity. Furthermore, the pharmacological profiles of ligands clearly indicate that these receptors may be considered as targets not only for selective but also for multi-target drugs that could be used for treatment of complex disorders such as Alzheimer's disease. Therefore, analysis of protein-ligand recognition in the binding site of histamine receptors and also other molecular targets has become a valuable tool in drug design toolkit. This review covers the period 2014-2020 in the field of theoretical investigations of histamine receptors mostly based on molecular modeling as well as the experimental characterization of novel ligands of these receptors.

Indexed as

AlgorithmsDrug DiscoveryMolecular Docking SimulationHistamine AgonistsHistamine AntagonistsHumansReceptors, HistamineHistamine AgonistsHistamine AntagonistsReceptors, Histaminecomputational studiesdrug discovery and designG protein-coupled receptorshistamine receptorsmolecular dockingvirtual screening

Identifiers

PMID33810008
PMCPMC8004658

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.