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Record W4414360555 · doi:10.1021/acs.jmedchem.5c00459

Comparative Profiling and Chemogenomics Application of Chemical Tools for NR4A Nuclear Receptors

2025· article· en· W4414360555 on OpenAlexfundno aff
Sabine Willems, Vasily Morozov, Julian A. Marschner, Daniel Merk

Bibliographic record

VenueJournal of Medicinal Chemistry · 2025
Typearticle
Languageen
FieldNeuroscience
TopicNuclear Receptors and Signaling
Canadian institutionsnot available
FundersEuropean Research CouncilInnovative Medicines InitiativeHorizon 2020 Framework ProgrammeKungliga Tekniska HögskolanDeutsche ForschungsgemeinschaftEuropean Federation of Pharmaceutical Industries and AssociationsDiamond Light SourceMcGill University
KeywordsNuclear receptorTranscription factorReceptorDrug discoveryEndoplasmic reticulumDrugProfiling (computer programming)

Abstract

fetched live from OpenAlex

The ligand-activated transcription factors of the NR4A family are implicated as promising drug targets with neuroprotective and anticancer potential and attract strong attention in drug discovery. Several NR4A modulators have been described, but a validated set of direct ligands for biological studies is lacking. Here, we profiled the reported and commercially available agonists and inverse agonists under uniform conditions in several orthogonal test systems to establish a highly annotated tool and gain comprehensive insights into the NR4A modulator characteristics. This comparative profiling revealed a lack of on-target binding and modulation for several putative NR4A ligands and validated a set of chemically diverse compounds as direct NR4A modulators for chemogenomics-based target identification studies. Prospective applications unveiled roles of NR4A receptors in endoplasmic reticulum stress and adipocyte differentiation, demonstrating suitability of the set to link the orphan targets with phenotypic effects.

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)0.001
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 score0.005
Threshold uncertainty score0.315

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.031
GPT teacher head0.300
Teacher spread0.269 · 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

Citations6
Published2025
Admission routes1
Has abstractyes

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