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Record W3092386371 · doi:10.1016/j.isci.2020.101643

Location Bias as Emerging Paradigm in GPCR Biology and Drug Discovery

2020· review· en· W3092386371 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueiScience · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicReceptor Mechanisms and Signaling
Canadian institutionsHôpital Maisonneuve-RosemontUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
FundersCanadian Institutes of Health Research
KeywordsDrug discoveryParadigm shiftComputational biologyData scienceBiologyComputer scienceBioinformaticsPhysics

Abstract

fetched live from OpenAlex

GPCRs are the largest receptor family that are involved in virtually all biological processes. Pharmacologically, they are highly druggable targets, as they cover more than 40% of all drugs in the market. Our knowledge of biased signaling provided insight into pharmacology vastly improving drug design to avoid unwanted effects and achieve higher efficacy and selectivity. However, yet another feature of GPCR biology is left largely unexplored, location bias. Recent developments in this field show promising avenues for evolution of new class of pharmaceuticals with greater potential for higher level of precision medicine. Further consideration and understanding of this phenomenon with deep biochemical and molecular insights would pave the road to success. In this review, we critically analyze this perspective and discuss new avenues of investigation.

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.997
Threshold uncertainty score0.642

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 head0.033
GPT teacher head0.327
Teacher spread0.293 · 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