Data Science Enables the Development of a New Class of Chiral Phosphoric Acid Catalysts.

Autor:	Liles JP; Department of Chemistry, University of Utah, 315 S 1400 E, Salt Lake City, UT, 84112, USA., Rouget-Virbel C; College of Chemistry, University of California, Berkeley, Berkeley, CA, 94720, USA., Wahlman JLH; Department of Chemistry, University of Utah, 315 S 1400 E, Salt Lake City, UT, 84112, USA., Rahimoff R; College of Chemistry, University of California, Berkeley, Berkeley, CA, 94720, USA., Crawford JM; Department of Chemistry, University of Utah, 315 S 1400 E, Salt Lake City, UT, 84112, USA., Medlin A; College of Chemistry, University of California, Berkeley, Berkeley, CA, 94720, USA., O'Connor V; Department of Chemistry, University of Utah, 315 S 1400 E, Salt Lake City, UT, 84112, USA., Li J; College of Chemistry, University of California, Berkeley, Berkeley, CA, 94720, USA., Roytman VA; College of Chemistry, University of California, Berkeley, Berkeley, CA, 94720, USA., Toste FD; College of Chemistry, University of California, Berkeley, Berkeley, CA, 94720, USA., Sigman MS; Department of Chemistry, University of Utah, 315 S 1400 E, Salt Lake City, UT, 84112, USA.; Lead contact.
Jazyk:	angličtina
Zdroj:	Chem [Chem] 2023 Jun 08; Vol. 9 (6), pp. 1518-1537. Date of Electronic Publication: 2023 Mar 24.
DOI:	10.1016/j.chempr.2023.02.020
Abstrakt:	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 er ). These techniques were then successfully applied towards two additional transforms. Taken together, these examples illustrate the power of combining rational design with data science ( ab initio ) to efficiently explore reactivity during catalyst development. Competing Interests: DECLARATION OF INTERESTS The authors declare no competing interests.
Databáze:	MEDLINE
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