In-silico-assisted derivatization of triarylboranes for the catalytic reductive functionalization of aniline-derived amino acids and peptides with H2

Yusei Hisata, Takashi Washio, Shinobu Takizawa, Sensuke Ogoshi and Yoichi Hoshimoto ()
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Yusei Hisata: Faculty of Engineering, Osaka University
Takashi Washio: SANKEN, Osaka University
Shinobu Takizawa: SANKEN, Osaka University
Sensuke Ogoshi: Faculty of Engineering, Osaka University
Yoichi Hoshimoto: Faculty of Engineering, Osaka University

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: Abstract Cheminformatics-based machine learning (ML) has been employed to determine optimal reaction conditions, including catalyst structures, in the field of synthetic chemistry. However, such ML-focused strategies have remained largely unexplored in the context of catalytic molecular transformations using Lewis-acidic main-group elements, probably due to the absence of a candidate library and effective guidelines (parameters) for the prediction of the activity of main-group elements. Here, the construction of a triarylborane library and its application to an ML-assisted approach for the catalytic reductive alkylation of aniline-derived amino acids and C-terminal-protected peptides with aldehydes and H2 is reported. A combined theoretical and experimental approach identified the optimal borane, i.e., B(2,3,5,6-Cl4-C6H)(2,6-F2-3,5-(CF3)2-C6H)2, which exhibits remarkable functional-group compatibility toward aniline derivatives in the presence of 4-methyltetrahydropyran. The present catalytic system generates H2O as the sole byproduct.

Date: 2024
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DOI: 10.1038/s41467-024-47984-0

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