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Recent Developed Nitrogen/Sulfur Heterocyclic Compounds with Marked andSelective Antiviral Activities (Microreview)

2023· article· en· W4385310768 on OpenAlexaff
Fathiy Mutalabisin, Mahta Ghafarikhaligh, Peyman Mihankhah, Nader Ghaffari Khaligh

Bibliographic record

VenueCurrent Organic Chemistry · 2023
Typearticle
Languageen
FieldChemistry
TopicSynthesis and Biological Evaluation
Canadian institutionsUniversity of Windsor
FundersMinistry of Higher Education, MalaysiaNippon Sheet Glass Foundation for Materials Science and Engineering
KeywordsChemistrySulfurViral infectionCombinatorial chemistryVirologyVirusOrganic chemistryBiology

Abstract

fetched live from OpenAlex

Abstract: Millions of deaths have been reported due to viral infections in medical history, and various viral infections are mentioned as the main cause of death. Although different types of research have been conducted to develop effective medication, there is a high demand to truly cure various viral infections. The resistance to the existence of antiviral drugs on the market is the main threat to human health, and an intrinsic demand to develop and synthesize new scaffolds is highly required to find less toxicity and high antiviral activity. Nitrogen-sulfur heterocyclic compounds have extensively exhibited efficient biological and pharmacological activity against viral species, and physicochemical and pharmacokinetic properties. In this microreview, recently developed nitrogen-sulfur heterocyclics and their performance with marked and selective antiviral activities are summarized. We hope this micro-review will help early scientists interested in the design of new compounds with selective and pronounced antiviral activities to identify and satisfy the necessary criteria for the further development of nitrogen-sulfur heterocyclic compounds.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.096
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.046
GPT teacher head0.286
Teacher spread0.240 · 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.

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

Citations11
Published2023
Admission routes1
Has abstractyes

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