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Record W2042881899 · doi:10.1017/s1466252308001527

Novel approaches to discovery of antibacterial agents

2008· review· en· W2042881899 on OpenAlexafffund
Patricia L. Taylor, Gerard D. Wright

Bibliographic record

VenueAnimal Health Research Reviews · 2008
Typereview
Languageen
FieldMedicine
TopicMicrobial Natural Products and Biosynthesis
Canadian institutionsMcMaster University
FundersCanadian Institutes of Health Research
KeywordsNatural productAntimicrobialAntibiotic resistanceAntibioticsSmall moleculeComputational biologyDrug discoveryGeneInfectious disease (medical specialty)ChemistryBiologyDiseaseMicrobiologyBioinformaticsBiochemistryMedicine

Abstract

fetched live from OpenAlex

Antimicrobial resistance is a rapidly increasing problem impacting the successful treatment of bacterial infectious disease. To combat resistance, the development of new treatment options is required. Recent advances in technology have aided in the discovery of novel antibacterial agents, specifically through genome mining for novel natural product biosynthetic gene clusters and improved small molecule high-throughput screening methods. Novel targets such as lipopolysaccharide and fatty acid biosyntheses have been identified by essential gene studies, representing a shift from traditional antibiotic targets. Finally, inhibiting non-essential genes with small molecules is being explored as a method for rescuing the activity of 'old' antibiotics, providing a novel synergistic approach to antimicrobial discovery.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.889
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.763
GPT teacher head0.520
Teacher spread0.243 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations30
Published2008
Admission routes2
Has abstractyes

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