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Record W4407597776 · doi:10.1016/j.mib.2025.102584

Targeted isolation of piperazate-containing molecules: bioinformatics and spectroscopy

2025· review· en· W4407597776 on OpenAlexafffund
Mostafa Hagar, Sangwook Kang, Raymond J. Andersen, Dong‐Chan Oh, Katherine S. Ryan

Bibliographic record

VenueCurrent Opinion in Microbiology · 2025
Typereview
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsUniversity of British Columbia HospitalUniversity of British ColumbiaSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyIsolation (microbiology)Computational biologyBioinformaticsMicrobiology

Abstract

fetched live from OpenAlex

Piperazic acid (Piz) is an intriguing hydrazine-containing amino acid found in a diverse variety of natural products, the majority of which are bioactive. Recently, several approaches have been reported for targeted isolation of Piz-containing molecules, combining spectroscopic techniques for screening Piz moieties with recent advances in Piz biosynthesis. Here, we highlight bioactive natural products recently isolated using these methods and bring into focus structural elucidation challenges impeding the discovery of more Piz-containing molecules.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.049
GPT teacher head0.389
Teacher spread0.339 · 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 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

Citations4
Published2025
Admission routes2
Has abstractyes

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