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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 OpenAlexafffund
Mohammad Ali Mohammad Nezhady, José Carlos Rivera, Sylvain Chemtob

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.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.003
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 head0.033
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

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 designNot applicable
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

Citations73
Published2020
Admission routes2
Has abstractyes

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