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

Hisata, Yusei; Washio, Takashi; Takizawa, Shinobu; Ogoshi, Sensuke; Hoshimoto, Yoichi

doi:https://doi.org/10.1038/s41467-024-47984-0

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

Hisata, Yusei; Washio, Takashi; Takizawa, Shinobu et al.

Nature Communications, 2024, 15, 3708

Number of Access:732（2025-07-26 12:17 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/95777

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Title	In-silico-assisted derivatization of triarylboranes for the catalytic reductive functionalization of aniline-derived amino acids and peptides with H₂
Creator	CreatorName Hisata, Yusei
	NRID 1000000192815 1000000192815 researchmap permalink 784791 784791 Scopus Author ID 7007039593 7007039593 CreatorName Washio, Takashi
	NRID 1000050324851 1000050324851 ORCID 0000-0002-9668-1888 0000-0002-9668-1888 researchmap permalink read0068650 read0068650 Scopus Author ID 7102334067 7102334067 CreatorName Takizawa, Shinobu
	NRID 1000030252589 1000030252589 researchmap permalink read0042792 read0042792 Scopus Author ID 7005303403 7005303403 CreatorName Ogoshi, Sensuke
	NRID 1000030710074 1000030710074 ORCID 0000-0003-0882-6109 0000-0003-0882-6109 researchmap permalink YHoshimoto YHoshimoto Scopus Author ID 36463455400 36463455400 CreatorName Hoshimoto, Yoichi
Description	Hisata Y., Washio T., Takizawa S., et al. In-silico-assisted derivatization of triarylboranes for the catalytic reductive functionalization of aniline-derived amino acids and peptides with H₂. Nature Communications 15, 3708 (2024); https://doi.org/10.1038/s41467-024-47984-0.
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.
Publisher	Nature Research
Source Title	Nature Communications
Volume (Issue)	15
Page	3708
Date of Issued	2024-05-07
Language	English
Handle URL	https://hdl.handle.net/11094/95777
eISSN	20411723
Relation.isIdenticalTo	DOI (Publisher Version) https://doi.org/10.1038/s41467-024-47984-0
Access Rights	open access
CopyRight	This article is licensed under a Creative Commons Attribution 4.0 International License.
oaire:version	Version of Record
Category	学術雑誌論文 Journal Article

Resource Type	journal article
Local Type	Journal Article
DCMI Type	Text
DCTERMS.bibliographicCitation	Nature Communications. 2024, 15, p. 3708
DC.title	In-silico-assisted derivatization of triarylboranes for the catalytic reductive functionalization of aniline-derived amino acids and peptides with H₂
DC.creator	Hisata, Yusei
	Washio, Takashi
	Takizawa, Shinobu
	Ogoshi, Sensuke
	Hoshimoto, Yoichi
DC.publisher	Nature Research
DC.language' scheme='DCTERMS.RFC1766	English
DC.type' scheme='DCTERMS.DCMIType	Text
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2024-05-07
DC.identifier	https://doi.org/10.1038/s41467-024-47984-0
DC.description	Hisata Y., Washio T., Takizawa S., et al. In-silico-assisted derivatization of triarylboranes for the catalytic reductive functionalization of aniline-derived amino acids and peptides with H₂. Nature Communications 15, 3708 (2024); https://doi.org/10.1038/s41467-024-47984-0.
DCTERMS.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.
DC.rights	This article is licensed under a Creative Commons Attribution 4.0 International License.
DC.format	95777
citation_title	In-silico-assisted derivatization of triarylboranes for the catalytic reductive functionalization of aniline-derived amino acids and peptides with H₂
citation_author	Hisata, Yusei
	Washio, Takashi
	Takizawa, Shinobu
	Ogoshi, Sensuke
	Hoshimoto, Yoichi
citation_publisher	Nature Research
citation_language	English
citation_date	2024-05-07
citation_journal_title	Nature Communications
citation_volume	15
citation_firstpage	3708
citation_doi	https://doi.org/10.1038/s41467-024-47984-0
citation_public_url	https://hdl.handle.net/11094/95777

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

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