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Sweet drugs Against Bad Bugs: Naphthoquinone-fused Enediyne Sugar Polysulfates for Nanomolar Inhibition of Coronavirus

2024· preprint· en· W4399922593 on OpenAlexaff
Xiaohua Huang, Miao Jing, Hongyu Zheng, Zhe Ding, Xinyu Yu, Jingtao Xu, Jiaming Lan, Aiguo Hu

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldChemistry
TopicCyclization and Aryne Chemistry
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsEnediyneCoronavirusChemistryCoronavirus disease 2019 (COVID-19)VirologyViral entryPharmacologyBiologyBiochemistryMedicineStereochemistryInfectious disease (medical specialty)VirusViral replication

Abstract

fetched live from OpenAlex

It has been four years since the emergence of the COVID-19 pandemic, and the ongoing threat it poses to human health and life un-derscores the continued need for the development of antiviral medications as a means of mitigating future viral outbreaks. In this study, we present a novel class of antiviral compounds known as naphthoquinone-fused enediyne sugar polysulfates, which have demonstrated efficacy against coronaviruses by targeting the conserved receptor binding domain on spike proteins. These compounds induce irreversible damage to the viral structure, resulting in inhibition of viral infection at nanomolar concentrations with minimal cytotoxic effects. Notably, the selectivity index of these compounds exceeds 50,000, suggesting significant potential for further de-velopment in antiviral therapeutics against coronavirus.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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)0.0030.000

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 head0.019
GPT teacher head0.277
Teacher spread0.258 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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