Data Science Enables the Development of a New Class of Chiral Phosphoric Acid Catalysts.
Journal
Chem
ISSN: 2451-9294
Titre abrégé: Chem
Pays: United States
ID NLM: 101688902
Informations de publication
Date de publication:
08 Jun 2023
08 Jun 2023
Historique:
pmc-release:
08
06
2024
medline:
31
7
2023
pubmed:
31
7
2023
entrez:
31
7
2023
Statut:
ppublish
Résumé
The widespread success of BINOL-chiral phosphoric acids (CPAs) has led to the development of several high molecular weight, sterically encumbered variants. Herein, we disclose an alternative, minimalistic chiral phosphoric acid backbone incorporating only a single instance of point chirality. Data science techniques were used to select a diverse training set of catalysts, which were benchmarked against the transfer hydrogenation of an 8-aminoquinoline. Using a univariate classification algorithm and multivariate linear regression, key catalyst features necessary for high levels of selectivity were deconvoluted, revealing a simple catalyst model capable of predicting selectivity for out-of-set catalysts. This workflow enabled extrapolation to a catalyst providing higher selectivity than both reported peptide-type and BINOL-type catalysts (up to 95:5
Identifiants
pubmed: 37519827
doi: 10.1016/j.chempr.2023.02.020
pmc: PMC10373836
mid: NIHMS1881262
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1518-1537Subventions
Organisme : NIGMS NIH HHS
ID : R35 GM136271
Pays : United States
Déclaration de conflit d'intérêts
DECLARATION OF INTERESTS The authors declare no competing interests.
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