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Record W4362633828 · doi:10.1038/s41467-023-37346-7

An α-chloroaldehyde-based formal synthesis of eribulin

2023· article· en· W4362633828 on OpenAlexafffund
Anissa Kaghad, Dimitrios Panagopoulos, Guillermo Caballero‐García, Huimin Zhai, Robert Britton

Bibliographic record

VenueNature Communications · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicChemical Synthesis and Analysis
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaMichael Smith Health Research BC
KeywordsEribulinStereocenterEpothilonesChemistryCombinatorial chemistryComputational biologyComputer scienceBiologyOrganic chemistryCatalysisCancerGenetics

Abstract

fetched live from OpenAlex

Eribulin (Halaven) is the most structurally complex non-peptidic drug made by total synthesis and has challenged preconceptions of synthetic feasibility in drug discovery and development. However, despite decades of research, the synthesis and manufacture of eribulin remains a daunting task. Here, we report syntheses of the most complex fragment of eribulin (C14-C35) used in two distinct industrial routes to this important anticancer drug. Our convergent strategy relies on a doubly diastereoselective Corey-Chaykovsky reaction to affect the union of two tetrahydrofuran-containing subunits. Notably, this process relies exclusively on enantiomerically enriched α-chloroaldehydes as building blocks for constructing the three densely functionalized oxygen heterocycles found in the C14-C35 fragment and all associated stereocenters. Overall, eribulin can now be produced in a total of 52 steps, which is a significant reduction from that reported in both academic and industrial syntheses.

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 head0.012
GPT teacher head0.294
Teacher spread0.282 · 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

Citations13
Published2023
Admission routes2
Has abstractyes

Explore more

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