MétaCan
Menu
Back to cohort
Record W2999167819 · doi:10.3390/molecules25020325

In Depth Analysis of Kinase Cross Screening Data to Identify CAMKK2 Inhibitory Scaffolds

2020· article· en· W2999167819 on OpenAlexfundno aff
Sean O’Byrne, John W. Scott, Joseph Pilotte, André da Silva Santiago, Christopher G. Langendorf, Jonathan S. Oakhill, Benjamin J. Eduful, Rafael M. Couñago, Carrow I. Wells, William J. Zuercher, Timothy M. Willson, David H. Drewry

Bibliographic record

VenueMolecules · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMelanoma and MAPK Pathways
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesNational Health and Medical Research CouncilNovartis PharmaNational Institutes of HealthMedical Research CouncilMinistero dello Sviluppo EconomicoGenome CanadaFundação de Amparo à Pesquisa do Estado de São PauloNational Cancer InstituteEuropean Federation of Pharmaceutical Industries and AssociationsMerck KGaAConselho Nacional de Desenvolvimento Científico e TecnológicoOntario Ministry of Economic Development and InnovationPfizer
KeywordsAMPKKinaseProtein kinase AEnzymeBiochemistryChemistryBiologyCell biology

Abstract

fetched live from OpenAlex

The calcium/calmodulin-dependent protein kinase kinase 2 (CAMKK2) activates CAMK1, CAMK4, AMPK, and AKT, leading to numerous physiological responses. The deregulation of CAMKK2 is linked to several diseases, suggesting the utility of CAMKK2 inhibitors for oncological, metabolic and inflammatory indications. In this work, we demonstrate that STO-609, frequently described as a selective inhibitor for CAMKK2, potently inhibits a significant number of other kinases. Through an analysis of literature and public databases, we have identified other potent CAMKK2 inhibitors and verified their activities in differential scanning fluorimetry and enzyme inhibition assays. These inhibitors are potential starting points for the development of selective CAMKK2 inhibitors and will lead to tools that delineate the roles of this kinase in disease biology

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.551

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.058
GPT teacher head0.341
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations37
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueMoleculesSame topicMelanoma and MAPK PathwaysFrench-language works237,207