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Record W2750864670 · doi:10.1021/acs.orglett.7b02476

Scalable Synthesis of a Key Intermediate for the Production of Pleuromutilin-Based Antibiotics

2017· article· en· W2750864670 on OpenAlexfundno aff
Stephen K. Murphy, Mingshuo Zeng, Seth B. Herzon

Bibliographic record

VenueOrganic Letters · 2017
Typearticle
Languageen
FieldVeterinary
TopicVeterinary medicine and infectious diseases
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesNatural Sciences and Engineering Research Council of CanadaYale University
KeywordsChemistryYield (engineering)Combinatorial chemistryReagentAntibioticsKey (lock)CatalysisScalabilityOrganic chemistryComputer scienceDatabaseBiochemistryComputer security

Abstract

fetched live from OpenAlex

An improved synthesis of an eneimide, which is a useful precursor to pleuromutilin-based antibiotics, is reported. This synthesis proceeds in six steps and 17% overall yield (27% based on recovery of a key hydrindenone intermediate) and requires two fewer chromatography steps and five fewer days of reaction time than the previously reported route. The use of expensive, acutely toxic, and precious metal reagents or catalysts has been minimized.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.041
GPT teacher head0.294
Teacher spread0.253 · 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 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
Published2017
Admission routes1
Has abstractyes

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