Evidence map›Paper›PMID 30200181›Full record

ArticleMolecules (Basel, Switzerland)2018

Discovery of High-Affinity Cannabinoid Receptors Ligands through a 3D-QSAR Ushered by Scaffold-Hopping Analysis.

Giuseppe Floresta, Orapan Apirakkan, Antonio Rescifina, Vincenzo Abbate

Abstract read
In one paragraph

Article in Molecules (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Artificial Intelligence Technologies for COVID-19 De Novo Drug Design.International journal of molecular sciences · 2022
    Review
  5. Article
  6. Article
  7. Article
  8. Machine learningRSC advances · 2021
    Article
  9. Virtual Screening ofInternational journal of molecular sciences · 2020
    Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. 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

4 authors.

Giuseppe FlorestaDepartment of Drug Sciences, University of Catania, V.le A. Doria, 95125 Catania, Italy. giuseppe.floresta@unict.it.ORCID 0000-0002-0668-1260
Orapan ApirakkanKing's Forensics, School of Population Health & Environmental Sciences, King's College London, Franklin-Wilkins Building, 150 Stamford Street, London SE1 9NH, UK. orapan.apirakkan@kcl.ac.uk.
Antonio RescifinaDepartment of Drug Sciences, University of Catania, V.le A. Doria, 95125 Catania, Italy. arescifina@unict.it.ORCID 0000-0001-5039-2151
Vincenzo AbbateKing's Forensics, School of Population Health & Environmental Sciences, King's College London, Franklin-Wilkins Building, 150 Stamford Street, London SE1 9NH, UK. vincenzo.abbate@kcl.ac.uk.ORCID 0000-0002-3300-0520

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Two 3D quantitative structure⁻activity relationships (3D-QSAR) models for predicting Cannabinoid receptor 1 and 2 (CB₁ and CB₂) ligands have been produced by way of creating a practical tool for the drug-design and optimization of CB₁ and CB₂ ligands. A set of 312 molecules have been used to build the model for the CB₁ receptor, and a set of 187 molecules for the CB₂ receptor. All of the molecules were recovered from the literature among those possessing measured

Indexed as

Models, MolecularQuantitative Structure-Activity RelationshipCannabinoid Receptor AgonistsCannabinoid Receptor AntagonistsDrug DesignHydrophobic and Hydrophilic InteractionsLigandsMolecular ConformationMolecular Docking SimulationMolecular Dynamics SimulationMolecular StructureProtein BindingReceptor, Cannabinoid, CB1Receptor, Cannabinoid, CB2Receptors, CannabinoidSoftwareCannabinoid Receptor AgonistsCannabinoid Receptor AntagonistsLigandsReceptor, Cannabinoid, CB1Receptor, Cannabinoid, CB2Receptors, Cannabinoid3D-QSARbioisosteric replacementscannabinoid receptorCB1 and CB2Forge and Spark softwarescaffold hoppingvirtual screening

Identifiers

PMID30200181
PMCPMC6225167

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.