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Utilization of Supervised Machine Learning to Understand Kinase Inhibitor Toxophore Profiles

UNC Libraries(2025)10.17615/mdfz-3h89Source: DataRank Database

Utilization of Supervised Machine Learning to Understand Kinase Inhibitor Toxophore Profiles is a research paper published in UNC Libraries (2025). On theSindex it has a DataRank of 0.

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (4 grants)Impact (FWCI 0.00)OA: greenTopics (22)IDs (PubMed, OpenAlex)SDGs (1)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (2)

Christopher R. M. Asquith ORCID,Andrew A. Bieberich

Live enrichment

PubMed:23304386 OpenAlex:https://openalex.org/W4416050788

National Science Foundation

Grant: 1726291

MRI: Acquisition of a Mass Spectrometer

National Institutes of Health

Grant: 5U24DK116204-04

Illuminating Function of the Understudied Druggable Kinome

European Commission

Grant: 115766

Unrestricted Leveraging of Targets for Research Advancement and Drug Discovery

Wellcome Trust

Grant: 106169

A UK Hub to Catalyse Open Target Discovery.

FWCI

0.00

Citation Percentile

0.3%

greenLicense: CC BY

repositoryhttps://doi.org/10.17615/mdfz-3h89

https://doi.org/10.3390/ijms24065088

https://dx.doi.org/10.17615/mdfz-3h89

https://pubmed.ncbi.nlm.nih.gov/36982163

http://dx.doi.org/10.3390/ijms24065088

Fields of Study

Computational Drug Discovery MethodsMelanoma and MAPK PathwaysCell Image Analysis Techniques

Keywords

KinomeKinaseTraining setLeverage (statistics)Supervised learningChemical spaceMechanism (biology)Pipeline (software)Adenosine TriphosphatetoxophoreDrug DiscoveryPhosphotransferaseskinase inhibitors4-anilinoquinazolineSupervised Machine Learning4-anilinoquinolineProtein Kinase InhibitorsArticlemachine learning drug discovery

Sustainable Development Goals

SDG 3: 3. Good health