Evidence map›Paper›PMID 32904416›Full record

ArticleData in brief2020

Kinase inhibitor data set for systematic analysis of representative kinases across the human kinome.

Oliver Laufkötter, Stefan Laufer, Jürgen Bajorath

Open access · goldAbstract read
In one paragraph

Article in Data in brief, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.7field-weighted citation impact, top 23% 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

4 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
  2. Article
  3. kinCSM: Using graph-based signatures to predict small molecule CDK2 inhibitors.Protein science : a publication of the Protein Society · 2022
    Article
  4. 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

3 authors at 2 institutions in 1 country.

Oliver LaufkötterDepartment of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Endenicher Allee 19c, Bonn D-53115, Germany.
Stefan LauferDepartment of Pharmacy and Biochemistry, Pharmaceutical/Medicinal Chemistry, TüCADD (Tübingen Center for Academic Drug Discovery), Eberhard Karls Universität Tübingen, Auf der Morgenstelle 8, Tübingen D-72076, Germany.
Jürgen BajorathDepartment of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Endenicher Allee 19c, Bonn D-53115, Germany.
University of Bonn · DEUniversity of Tübingen · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A large set of multi-kinase inhibitors with high-confidence activity data was assembled and used to generate network representations revealing kinase relationships based upon shared inhibitors [1]. Compounds and activity annotations were originally selected from public repositories and organized in an in-house database from which the data set was extracted and curated. The new data set comprises more than 36,000 inhibitors with multiple activity annotations for a total of 420 human kinases (providing 81% coverage of the human kinome), representing a total of ∼127,000 kinase-inhibitor interactions. Use of the data is not limited to the network application reported in [1]. It can also be used, for example, for different types of compound promiscuity analysis or machine learning (such a multi-task modeling). In addition, the data set provides a large resource for complementing kinase drug discovery projects with external compound information.

Indexed as

Activity annotationsCompound-kinase interactionsHuman kinomeMulti-kinase inhibitorsNetwork representations

Identifiers

PMID32904416
PMCPMC7452594
OpenAlexW3048981931

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.