MétaCan
Menu
Back to cohort
Record W4200519260 · doi:10.26434/chemrxiv-2021-8nlfl

Concise syntheses of GB22, GB13 and himgaline by cross-coupling and complete reduction

2021· preprint· en· W4200519260 on OpenAlexfundno aff
Eleanor M. Landwehr, Meghan A. Baker, Takuya Oguma, Hannah E. Burdge, Takahiro Kawajiri, Ryan A. Shenvi

Bibliographic record

VenueChemRxiv · 2021
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBioactive natural compounds
Canadian institutionsnot available
FundersDivision of ChemistryShionogiConcordia University of EdmontonJapan Society for the Promotion of ScienceIndustrial Technology Research InstituteBristol-Myers SquibbNational Institutes of HealthNational Science Foundation
KeywordsStereoselectivityReduction (mathematics)Coupling (piping)Combinatorial chemistryChemistryCoupling reactionFraction (chemistry)StereochemistryOrganic chemistryMathematicsMaterials science

Abstract

fetched live from OpenAlex

Class III neuroactive metabolites from the bark of Galbu-limima belgraveana occur in variable distribution and are not easily procured by chemical synthesis. Here we decrease the synthetic burden of himgaline to nearly one-third of the prior best (7–9 vs. 19–31 steps) by cross-coupling high fraction aromatic (FAr) building blocks followed by com-plete, stereoselective reduction to high-fraction sp3 (Fsp3) products. This short entry into GB alkaloid space allows its extensive exploration and biological interrogation.

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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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)0.0040.002

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 head0.024
GPT teacher head0.288
Teacher spread0.264 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueChemRxivSame topicBioactive natural compoundsFrench-language works237,207