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Record W4384069906 · doi:10.1021/acscentsci.3c00616

A Route to Potent, Selective, and Biased Salvinorin Chemical Space

2023· article· en· W4384069906 on OpenAlexfundno aff
Sarah J. Hill, Nathan Dao, Vương Đặng Quốc, Edward L. Stahl, Laura Bohn, Ryan A. Shenvi

Bibliographic record

VenueACS Central Science · 2023
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicAlkaloids: synthesis and pharmacology
Canadian institutionsnot available
FundersNational Institute on Drug AbuseNatural Sciences and Engineering Research Council of CanadaUniversity of California, San DiegoNational Institute of General Medical SciencesDirectorate for Biological SciencesLanzhou University
KeywordsChemistrySteric effectsCombinatorial chemistryChemical spaceSelectivityNucleophileCycloadditionStereochemistryAlkyneDrug discoveryOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

The salvinorins serve as templates for next generation analgesics, antipruritics, and dissociative hallucinogens via selective and potent agonism of the kappa-opioid receptor (KOR). In contrast to most opioids, the salvinorins lack basic amines and bind with high affinity and selectivity via complex polyoxygenated scaffolds that have frustrated deep-seated modification by synthesis. Here we describe a short asymmetric synthesis that relies on a sterically confined organocatalyst to dissociate acidity from reactivity and effect Robinson annulation of an unactivated nucleophile/unstable electrophile pair. Combined with a cobalt-catalyzed polarized diene-alkyne cycloaddition, the route allows divergent access to a focused library of salvinorins. We appraise the synthesis by its generation of multiple analogs that exceed the potency, selectivity, stability, and functional bias of salvinorin A itself.

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 score0.007

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 science0.0000.000
Research integrity0.0000.000
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 head0.092
GPT teacher head0.424
Teacher spread0.331 · 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

Citations26
Published2023
Admission routes1
Has abstractyes

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Same venueACS Central ScienceSame topicAlkaloids: synthesis and pharmacologyFrench-language works237,207