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Record W1974421682 · doi:10.1021/acs.accounts.5b00045

Exploiting Metal–Ligand Bifunctional Reactions in the Design of Iron Asymmetric Hydrogenation Catalysts

2015· article· en· W1974421682 on OpenAlexafffund
Robert H. Morris

Bibliographic record

VenueAccounts of Chemical Research · 2015
Typearticle
Languageen
FieldChemistry
TopicAsymmetric Hydrogenation and Catalysis
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryBifunctionalEnantiopure drugCatalysisLigand (biochemistry)Combinatorial chemistryBifunctional catalystAmine gas treatingEnantioselective synthesisTransfer hydrogenationSteric effectsRutheniumHydrideOrganic chemistryAsymmetric hydrogenationArylMetal

Abstract

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This is an Account of our development of iron-based catalysts for the asymmetric transfer hydrogenation (ATH) and asymmetric pressure hydrogenation (AH) of ketones and imines. These chemical processes provide enantiopure alcohols and amines for use in the pharmaceutical, agrochemical, fragrance, and other fine chemical industries. Fundamental principles of bifunctional reactivity obtained by studies of ruthenium catalysts by Noyori's group and our own with tetradentate ligands with tertiary phosphine and secondary amine donor groups were applied to improve the performance of these first iron(II) catalysts. In particular the correct positioning of a bifunctional H-Fe-NH unit in an iron hydride amine complex leads to exceptional catalyst activity because of the low energy barrier of dihydrogen transfer to the polar bond of the substrate. In addition the ligand structure with this NH group along with an asymmetric array of aryl groups orients the incoming substrate by hydrogen-bonding, and steric interactions provide the hydrogenated product in high enantioselectivity for several classes of substrates. Enantiomerically pure diamines or diphenylphosphino-amine compounds are used as the source of the asymmetry in the tetradentate ligands formed by the condensation of the amines with dialkyl- or diaryl-phosphinoaldehydes, a synthesis that is templated by Fe(II). The commercially available ortho-diphenylphosphinobenzaldehyde was used in the initial studies, but then diaryl-phosphinoacetaldehydes were found to produce much more effective ligands for iron(II). Once the mechanism of catalysis became clearer, the iron-templated synthesis of (S,S)-PAr2CH2CH2NHCHPhCHPhNH2 ligand precursors was developed to specifically introduce a secondary amine in the precatalyst structures. The reaction of a precatalyst with strong base yields a key iron-amido complex that reacts with isopropanol (in ATH) or dihydrogen (in AH) to generate an iron hydride with the Fe-H bond parallel to the secondary amine N-H. In the AH reactions, the correct acidity of the intermediate iron-dihydrogen complex and correct basicity of the amide are important factors for the heterolytic splitting of the dihydrogen to generate the H-Fe-N-H unit; the acidity of dihydrogen complexes including those found in hydrogenases can be estimated by a simple additive ligand acidity constant method. The placement of the hydridic-protonic Fe-H···HN interaction in the asymmetric catalyst structure influences the enantioinduction. The sense of enantioinduction is predictable from the structure of the H-Fe-N-H-containing catalyst interacting with the ketone in the same way as related H-Ru-N-H-containing catalysts. The modular construction of the catalysts permits large variations in order to produce alcohol or amine products with enantiomeric excess in the 90-100% range in several cases.

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 score0.001
Threshold uncertainty score0.003

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.137
GPT teacher head0.362
Teacher spread0.225 · 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

Citations420
Published2015
Admission routes2
Has abstractyes

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