Evidence map›Paper›PMID 32392873›Full record

ArticleBiomolecules2020

New Insights into Key Determinants for Adenosine 1 Receptor Antagonists Selectivity Using Supervised Molecular Dynamics Simulations.

Giovanni Bolcato, Maicol Bissaro, Giuseppe Deganutti, Mattia Sturlese, Stefano Moro

Open access · goldAbstract read
In one paragraph

Article in Biomolecules, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.8field-weighted citation impact, top 31% of its field
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

3 citing papers in PubMed, 10 citations in OpenAlex.

  1. Ligand-Directed Labeling of the Adenosine AJournal of medicinal chemistry · 2024
    Article
  2. "Dual Anta-Inhibitors" of the APharmaceuticals (Basel, Switzerland) · 2023
    Article
  3. Review
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

5 authors at 2 institutions in 2 countries.

Giovanni BolcatoMolecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, 35131 Padova, Italy.
Maicol BissaroMolecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, 35131 Padova, Italy.
Giuseppe DeganuttiSchool of Life Sciences, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK.
Mattia SturleseMolecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, 35131 Padova, Italy.
Stefano MoroMolecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, 35131 Padova, Italy.
University of Padua · ITUniversity of Essex · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Adenosine receptors (ARs), like many otherGprotein-coupledreceptors (GPCRs), are targets of primary interest indrug design. However, one of the main limits for the development of drugs for this class of GPCRs is the complex selectivity profile usually displayed by ligands. Numerous efforts have been madefor clarifying the selectivity of ARs, leading to the development of many ligand-based models. The structure of the AR subtype A

Indexed as

Molecular Dynamics SimulationAdenosine A1 Receptor AntagonistsBinding SitesHumansMolecular Docking SimulationProtein BindingReceptor, Adenosine A1Supervised Machine LearningAdenosine A1 Receptor AntagonistsReceptor, Adenosine A1A1ARA2AARadenosine receptorsantagonistGPCRsmoleculardynamics (MD)selectivitysupervised molecular dynamics (SuMD)

Identifiers

PMID32392873
PMCPMC7278174
OpenAlexW3020826672

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.