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Record W4389817477 · doi:10.1016/j.gresc.2023.12.001

Synthesis of dydrogesterone by aromatization-dearomatization strategy

2023· article· en· W4389817477 on OpenAlexfundno aff
Heng Bai, Wei Gu, Di Zhao, Guangqing Xu, Wenjun Tang

Bibliographic record

VenueGreen Synthesis and Catalysis · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSteroid Chemistry and Biochemistry
Canadian institutionsnot available
FundersNational Key Research and Development Program of ChinaYouth Innovation Promotion AssociationChinese Academy of SciencesNational Natural Science Foundation of ChinaCanadian Anesthesiologists' Society
KeywordsAromatizationDydrogesteroneChemistryBiochemical engineeringCombinatorial chemistryOrganic chemistryCatalysisEngineeringMedicineInternal medicine

Abstract

fetched live from OpenAlex

Dydrogesterone as an agonist of the progesterone receptor is an important and selective synthetic progesterone used for the treatment of a variety of conditions associated with progesterone deficiency including menstrual cycle regulation, infertility, and prevention of miscarriage. Its manufacturing process employing photochemical reactions remains a significant challenge. Herein we report the first total synthesis of dydrogesterone via a key 10α-substitution-selective dearomative cyclizationfree of photochemical protocols starting from Hajos-Parrish ketone. A gram-scale synthesis is also accomplished from readily available 9-hydroxy-4-androstene-3,17-dione through a novel aromatization-dearomatization strategy of ring A in steroid chemistry. Key synthetic features include a facile chemical aromatization of 9-hydroxy-4-androstene-3,17-dione, efficient ligand-controlled asymmetric dearomative cyclization to install the 10α-Me group, and an effective hydroxyl-directed hydrogenation of sterically congested tetrasubstituted olefin to establish the 8β-H,9β-H stereochemistry.

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 score0.008

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 head0.007
GPT teacher head0.215
Teacher spread0.208 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueGreen Synthesis and CatalysisSame topicSteroid Chemistry and BiochemistryFrench-language works237,207