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Record W4415836614 · doi:10.62051/gvs2pj23

Covalent Inhibitors Targeting FabH: A Cutting-edge Strategy in the Development of Novel Antibiotics

2025· article· W4415836614 on OpenAlexaff

Bibliographic record

VenueTransactions on Materials Biotechnology and Life Sciences · 2025
Typearticle
Language
FieldChemistry
TopicAntimicrobial agents and applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCovalent bondAntibioticsAntibiotic resistanceEnzymeDrug resistanceMechanism (biology)Mechanism of action

Abstract

fetched live from OpenAlex

Antibiotic resistance has become a global public health crisis, and there is an urgent need to develop new mechanism antibacterial drugs targeting specific targets of bacteria. β -ketoacyl-ACP synthase Ⅲ (FabH) inhibitors have shown great potential to overcome bacterial resistance and have become an emerging hot field in the research and development of anti-infective drugs. And unlike traditional reversible inhibitors (relying on transient non-covalent interactions), covalent inhibitors can form stable covalent bonds to achieve irreversible inhibition of the target. This combination method brings significant advantages (such as long-term effectiveness and high efficiency, etc.). This article systematically reviews the uniqueness of FabH, analyzes the chemical structure and application of representative covalent inhibitors of FabH, dissects the mechanism of action of covalent inhibitors and the clinical evidence that FabH can be used as a covalent inhibitory target. The research on covalent inhibitors based on FabH provides a promising new approach for the development of the next generation of novel antibiotics that are highly efficient, narrow-spectrum and less prone to drug resistance.

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)0.000
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 score0.030
Threshold uncertainty score0.793

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.028
GPT teacher head0.285
Teacher spread0.257 · 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 routes1
Has abstractyes

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