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Record W1966623772 · doi:10.1517/17460441.3.6.623

Functional screening of α7 nicotinic receptor ligands

2008· article· en· W1966623772 on OpenAlexaff
John Dunlop, Ravikumar Peri, Georg C. Terstappen, Mark R. Bowlby

Bibliographic record

VenueExpert Opinion on Drug Discovery · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNicotinic Acetylcholine Receptors Study
Canadian institutionsWomen's Health Research Institute
Fundersnot available
KeywordsNicotinic agonistDrug discoveryNicotinic acetylcholine receptorIon channelNeuroscienceAcetylcholine receptorReceptorPharmacologyDrug targetAlpha-4 beta-2 nicotinic receptorComputational biologyBiologyChemistryBioinformaticsBiochemistry

Abstract

fetched live from OpenAlex

BACKGROUND: The α7 nicotinic acetylcholine receptor, a ligand-gated ion channel, is an attractive drug discovery target in schizophrenia and Alzheimer's disease. OBJECTIVE: We have evaluated the various approaches to discovering ligands targeting the α7 nicotinic receptor to define the current paradigm driving drug discovery efforts in this area. METHODS: Assays using functional read-outs as a consequence of α7 nicotinic receptor activation have been reviewed. CONCLUSION: Functional assays using fluorescence-based optical methods in combination with direct electrophysiological recordings of channel function currently provide an integrated approach to the discovery of α7 nicotinic receptor targeted ligands.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.350
Threshold uncertainty score0.912

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.026
GPT teacher head0.277
Teacher spread0.251 · 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

Citations1
Published2008
Admission routes1
Has abstractyes

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