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Record W3040511919 · doi:10.51847/q3egbyl

10.51847/Q3eGByl

2000· article· en· W3040511919 on OpenAlexvenueno aff
Keri Lestari, Trully Deti Rose Sitorus, Sandra Megantara, Jutti Levita

Bibliographic record

VenueTime to knit · 2000
Typearticle
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsnot available
Fundersnot available
KeywordsHydroxychloroquineChloroquineQuinineAngiotensin-converting enzyme 2Docking (animal)Coronavirus disease 2019 (COVID-19)PharmacologyEnzymeVirologyAntidoteSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakChemistryBiologyMedicineMalariaBiochemistryInternal medicineImmunologyToxicityInfectious disease (medical specialty)Veterinary medicine

Abstract

fetched live from OpenAlex

Researches on drugs to be used for COVID-19 therapy are still challenging. SARS-CoV-2 uses the angiotensin-converting enzyme (ACE2) receptor to enter the human body. The S (spike) protein structure of SARS-CoV-2 interacts with the active site of ACE2 receptor, defined as the peptidase domain, which consists of Gln24, Asp30, His34, Tyr41, Gln42, Met82, Lys353, Arg357. This work studied the interaction of three quinoline-based antimalarial drugs with the peptidase domain of ACE2 receptor. The X-ra

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.020
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.003
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.9800.982

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 head0.010
GPT teacher head0.229
Teacher spread0.218 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations21
Published2000
Admission routes1
Has abstractyes

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