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Record W3000417149 · doi:10.1101/2020.01.08.883009

In depth analysis of kinase cross screening data to identify CAMKK2 inhibitory scaffolds

2020· preprint· en· W3000417149 on OpenAlexfundno aff
Sean O’Byrne, John W. Scott, Joseph Pilotte, André de S. 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

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2020
Typepreprint
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsnot available
FundersNovartis PharmaNational Cancer InstituteNational Institutes of HealthMinistero dello Sviluppo EconomicoMedical Research CouncilEuropean Federation of Pharmaceutical Industries and AssociationsMerck KGaAOntario Ministry of Economic Development and InnovationGenome CanadaNational Health and Medical Research CouncilPfizer
KeywordsAMPKProtein kinase AKinaseBiologyCell biology

Abstract

fetched live from OpenAlex

Abstract The calcium/calmodulin-dependent protein kinase kinase 2 (CAMKK2) plays a central role in many cell signaling pathways. CAMKK2 activates CAMK1, CAMK4, AMPK, and AKT leading to numerous physiological responses. Deregulation of CAMKK2 is linked to several diseases, suggesting utility of CAMKK2 inhibitors for oncological, metabolic and inflammatory indications. In this work we review the role of CAMKK2 in biology and disease. Through analysis of literature and public databases we have identified starting points for CAMKK2 inhibitor medicinal chemistry campaigns. These starting points provide an opportunity 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.069
GPT teacher head0.350
Teacher spread0.282 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations11
Published2020
Admission routes1
Has abstractyes

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