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Record W4412534476 · doi:10.1016/j.ejmcr.2025.100288

Triazenes as inhibitors of HIV-1 and HCoV-OC43: A structure-activity relationship study

2025· article· en· W4412534476 on OpenAlexafffund
Natacha Mérindol, Saeedeh Hashemian, Seynabou Sokhna, Marie-Pierre Girard, Marc Presset, Insa Seck, Lalla A. Ba, Seydou Ka, Samba Fama Ndoye, Issa Samb, Erwan Le Gall, Lionel Berthoux, Matar Seck, Isabel Desgagné‐Penix

Bibliographic record

VenueEuropean Journal of Medicinal Chemistry Reports · 2025
Typearticle
Languageen
FieldChemistry
TopicChemical Reactions and Mechanisms
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersCanada Excellence Research Chairs, Government of CanadaCanada Research Chairs
KeywordsHuman immunodeficiency virus (HIV)VirologyBiology

Abstract

fetched live from OpenAlex

Triazenes, or amino-substituted diazenes, are organic compounds containing three contiguous nitrogen atoms, that have potent biological activities. We previously demonstrated that triazenes, particularly those substituted with a phenyl or 3-pyridyl ring at the 1-position and a 2-pyridyl ring at the 3-position, exhibit anti-DENV properties. Here, we evaluated the antiviral activity against a betacoronavirus (HCoV-OC43) and a lentivirus (HIV-1). 1-(4-trifluoromethylphenyl)-2-imidazol-1-yldiazene ( 21 ) exhibited broad-spectrum activity (EC 50 = 6.6–6.8 μM) but was cytotoxic to THP-1 cells. Pyridyl triazenes ( 14, 15 ) were the most potent against HCoV-OC43, while 1-(4-methoxyphenyl)-2-morpholin-4-yldiazene ( 6 ) and 1-(4-methoxyphenyl)3-(-6-methylpyridin-2-yl)triazene ( 10 ) inhibited HIV-1 the most. Structure–activity relationship analysis, supported by molecular docking, indicated that para -methoxy groups favored interactions with viral enzyme binding pockets, enhancing antiviral potency, while meta and para -trifluoromethyl groups were associated with reduced activity and increased cytotoxicity. These findings support the further development of triazenes as antiviral scaffolds.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.023
Threshold uncertainty score0.666

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 integrity0.0000.001
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 head0.013
GPT teacher head0.264
Teacher spread0.251 · 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.

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
Published2025
Admission routes2
Has abstractyes

Explore more

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