Platinum-Catalyzed Direct Amination of Allylic Alcohols

Ohshima, Takashi; Mashima, Kazushi

doi:10.5059/yukigoseikyokaishi.70.1145

Cited by 5 publications

(1 citation statement)

References 80 publications

(17 reference statements)

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“…[2][3][4] At the same time, Pt complexes are well-established (pre)catalysts widely used in the laboratory and industry. Platinum catalysts for reactions such as C-Si [5][6][7][8] and C-B [7,[9][10][11][12][13] bond formation and allylic alcohol amination [14][15][16][17] were thoroughly developed. Another remarkable usage area of Pt compounds is pharmaceutics, where water-soluble Pt complexes with simple organic ligands gave rise to the new generation of anticancer drugs.…”

Section: Introductionmentioning

confidence: 99%

Predicting ¹⁹⁵Pt NMR Chemical Shifts in Water‐Soluble Inorganic/Organometallic Complexes with a Fast and Simple Protocol Combining Semiempirical Modeling and Machine Learning

2023

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Water-soluble Pt complexes are the key components in medicinal chemistry and catalysis. The well-known cisplatin family of anticancer drugs and industrial hydrosylilation catalysts are two leading examples. On the molecular level, the activity mechanisms of such complexes mostly involve changes in the Pt coordination sphere. Using 195 Pt NMR spectroscopy for operando monitoring would be a valuable tool for uncovering the activity mechanisms; however, reliable approaches for the rapid correlation of Pt complex structure with 195 Pt chemical shifts are very challenging and not available for everyday research practice. While NMR shielding is a response property, molecular 3D structure determines NMR spectra, as widely known, which allows us to build up 3D structure to 195 Pt chemical shift correlations. Accordingly, we present a new workflow for the determination of lowest-energy configurational/conformational isomers based on the GFN2-xTB semiempirical method and prediction of corresponding chemical shifts with a Machine Learning (ML) model tuned for Pt complexes. The workflow was designed for the prediction of 195 Pt chemical shifts of water-soluble Pt(II) and Pt(IV) anionic, neutral, and cationic complexes with halide, NO 2 À , (di)amino, and (di)carboxylate ligands with chemical shift values ranging from À 6293 to 7090 ppm. The model offered an accuracy (normalized root-mean-square deviation/RMSD) of 1.08 %/ 145.02 ppm on the held-out test set.

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Section: Introductionmentioning

confidence: 99%

Predicting ¹⁹⁵Pt NMR Chemical Shifts in Water‐Soluble Inorganic/Organometallic Complexes with a Fast and Simple Protocol Combining Semiempirical Modeling and Machine Learning

2023

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H‐*BEA Zeolite‐Catalyzed Nucleophilic Substitution in Allyl Alcohols Using Sulfonamides, Amides, and Anilines

et al. 2020

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Atom Economy and Reaction Mass Efficiency

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2014

SpringerBriefs in Molecular Science

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Platinum-Catalyzed Direct Amination of Allylic Alcohols

Cited by 5 publications

References 80 publications

Predicting ¹⁹⁵Pt NMR Chemical Shifts in Water‐Soluble Inorganic/Organometallic Complexes with a Fast and Simple Protocol Combining Semiempirical Modeling and Machine Learning

Predicting ¹⁹⁵Pt NMR Chemical Shifts in Water‐Soluble Inorganic/Organometallic Complexes with a Fast and Simple Protocol Combining Semiempirical Modeling and Machine Learning

H‐*BEA Zeolite‐Catalyzed Nucleophilic Substitution in Allyl Alcohols Using Sulfonamides, Amides, and Anilines

Atom Economy and Reaction Mass Efficiency

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Platinum-Catalyzed Direct Amination of Allylic Alcohols

Cited by 5 publications

References 80 publications

Predicting 195Pt NMR Chemical Shifts in Water‐Soluble Inorganic/Organometallic Complexes with a Fast and Simple Protocol Combining Semiempirical Modeling and Machine Learning

Predicting 195Pt NMR Chemical Shifts in Water‐Soluble Inorganic/Organometallic Complexes with a Fast and Simple Protocol Combining Semiempirical Modeling and Machine Learning

H‐*BEA Zeolite‐Catalyzed Nucleophilic Substitution in Allyl Alcohols Using Sulfonamides, Amides, and Anilines

Atom Economy and Reaction Mass Efficiency

Contact Info

Product

Resources

About

Predicting ¹⁹⁵Pt NMR Chemical Shifts in Water‐Soluble Inorganic/Organometallic Complexes with a Fast and Simple Protocol Combining Semiempirical Modeling and Machine Learning

Predicting ¹⁹⁵Pt NMR Chemical Shifts in Water‐Soluble Inorganic/Organometallic Complexes with a Fast and Simple Protocol Combining Semiempirical Modeling and Machine Learning