Evidence map›Paper›PMID 30088101›Full record

ArticleJournal of molecular modeling2018

Computational design of new protein kinase 2 inhibitors for the treatment of inflammatory diseases using QSAR, pharmacophore-structure-based virtual screening, and molecular dynamics.

Josiane V Cruz, Rodolfo B Serafim, Gabriel M da Silva, Silvana Giuliatti, Joaquín M C Rosa, Moysés F Araújo Neto, Franco H A Leite, Carlton A Taft, Carlos H T P da Silva, Cleydson B R Santos

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PubMed Publisher
In one paragraph

Article in Journal of molecular modeling, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
3.4field-weighted citation impact, top 7% 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

18 citing papers in PubMed, 32 citations in OpenAlex.

  1. Article
  2. Heliyon · 2023
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  4. Papaverinol-Molecules (Basel, Switzerland) · 2023
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  5. Identification of Potential NewInternational journal of molecular sciences · 2022
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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

10 authors at 6 institutions in 2 countries.

Josiane V CruzPrograma de Pós-graduação em Ciências Farmacêuticas, Universidade Federal do Amapá, Macapá, Amapá, 68902-280, Brazil. josianeviana2007@gmail.com.ORCID http://orcid.org/0000-0002-2505-5134
Rodolfo B SerafimDepartamento de Biologia Celular e Molecular e Bioagentes Patogênicos, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo (USP), São Paulo, 14049-900, Brazil.
Gabriel M da SilvaGrupo de Bioinformática, Departamento de Genética, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo (USP), São Paulo, 14049-900, Brazil.
Silvana GiuliattiGrupo de Bioinformática, Departamento de Genética, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo (USP), São Paulo, 14049-900, Brazil.
Joaquín M C RosaDepartment of Pharmaceutical and Organic Chemistry, Faculty of Pharmacy, Campus of Cartuja, s/n, University of Granada, 18071, Granada, Spain.
Moysés F Araújo NetoLaboratório de Modelagem Molecular, Universidade Estadual de Feira de Santana, Feira de Santana, Bahia, 44036-900, Brazil.
Franco H A LeiteLaboratório de Modelagem Molecular, Universidade Estadual de Feira de Santana, Feira de Santana, Bahia, 44036-900, Brazil.
Carlton A TaftCentro Brasileiro de Pesquisas Físicas, Rua Dr. Xavier Sigaud, Rio de Janeiro, 22290-180, Brazil.
Carlos H T P da SilvaLaboratório de Química Farmacêutica Computacional, Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, São Paulo, 14040-020, Brazil.
Cleydson B R SantosPrograma de Pós-graduação em Ciências Farmacêuticas, Universidade Federal do Amapá, Macapá, Amapá, 68902-280, Brazil. breno@unifap.br.
Universidade de São Paulo · BRUniversidade Estadual de Feira de Santana · BRCentro Brasileiro de Pesquisas Físicas · BRUniversidad de Granada · ESUniversidade de Ribeirão Preto · BRUniversidade Federal do Amapá · BR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Receptor-interacting protein kinase 2 (RIPK2) plays an essential role in autoimmune response and is suggested as a target for inflammatory diseases. A pharmacophore model was built from a dataset with ponatinib (template) and 18 RIPK2 inhibitors selected from BindingDB database. The pharmacophore model validation was performed by multiple linear regression (MLR). The statistical quality of the model was evaluated by the correlation coefficient (R), squared correlation coefficient (R

Indexed as

Molecular Docking SimulationMolecular Dynamics SimulationReceptor-Interacting Protein Serine-Threonine Kinase 2Cell LineDrug Evaluation, PreclinicalHumansInflammationProtein Kinase InhibitorsProtein Kinase InhibitorsReceptor-Interacting Protein Serine-Threonine Kinase 2RIPK2 protein, humanAnti-inflammatory drugsMolecular dynamicsPharmacokineticRIPK2Toxicological

Identifiers

PMID30088101
OpenAlexW2885125942

What OpenQuestion holds

Textmetadata
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.