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Record W2151795220 · doi:10.1042/bst0351027

Structure-based optimization of PKCθ inhibitors

2007· review· en· W2151795220 on OpenAlexaff
Lidia Mosyak, Zhang‐Bao Xu, Diane Joseph‐McCarthy, Natasja Brooijmans, W.S. Somers, Divya Chaudhary

Bibliographic record

VenueBiochemical Society Transactions · 2007
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Kinase Regulation and GTPase Signaling
Canadian institutionsWomen's Health Research Institute
Fundersnot available
KeywordsProtein kinase CChemistryPharmacologyComputational biologyCell biologyComputer scienceBiochemistryBiologyKinase

Abstract

fetched live from OpenAlex

PKCtheta (protein kinase Ctheta) is a central signalling molecule in the T-cell receptor activation pathway and is a target for treatment of a number of diseases. Several PKC inhibitors are in the drug-discovery pharmaceutical programmes today for the treatment of cancer, diabetes and arthritis. CD4(+) T-lymphocytes also play a critical role in the initiation and progression of allergic airway inflammation. Our goal is the development of PKCtheta antagonists as a means to control asthma and autoimmune diseases, using the strategy based on developing small-molecule agents that would block the enzyme's catalytic activity. Here, we discuss our work on the discovery of lead chemical series and review our X-ray structural and modelling approaches, including a structure-surrogate strategy that helped guide us in the lead compound optimizations.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.846
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.023
GPT teacher head0.296
Teacher spread0.273 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations10
Published2007
Admission routes1
Has abstractyes

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