Developing a design guideline of boronic acid derivatives to scavenge targeted sugars in the formose reaction products using DFT-based machine learning

Ishihara, Nanako; Chikatani, Genta; Nishijima, Hiroaki; Tabata, Hiro; Hase, Yoko; Mukouyama, Yoshiharu; Nakanishi, Shuji; Mukaida, Shiho

doi:https://doi.org/10.1093/chemle/upae087

Developing a design guideline of boronic acid derivatives to scavenge targeted sugars in the formose reaction products using DFT-based machine learning

Ishihara, Nanako; Chikatani, Genta; Nishijima, Hiroaki et al.

Chemistry Letters, 2024, 53(6), upae087

Number of Access:540（2025-08-28 21:08 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/97753

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Title	Developing a design guideline of boronic acid derivatives to scavenge targeted sugars in the formose reaction products using DFT-based machine learning
Creator	CreatorName Ishihara, Nanako
	CreatorName Chikatani, Genta
	CreatorName Nishijima, Hiroaki
	CreatorName Tabata, Hiro
	CreatorName Hase, Yoko
	NRID 1000090339078 1000090339078 CreatorName Mukouyama, Yoshiharu
	NRID 1000040333447 1000040333447 ORCID 0000-0002-3313-2689 0000-0002-3313-2689 researchmap permalink read0191166 read0191166 Scopus Author ID 7402706228 7402706228 CreatorName Nakanishi, Shuji
	CreatorName Mukaida, Shiho
Subject	DFT calculation
	formose reaction
	machine learning
Description	This is a pre-copyedited, author-produced version of an article accepted for publication in Chemistry Letters following peer review. The version of record Ishihara N., Chikatani G., Nishijima H., et al. Developing a design guideline of boronic acid derivatives to scavenge targeted sugars in the formose reaction products using DFT-based machine learning. Chemistry Letters 53, upae087 (2024) is available online at: https://doi.org/10.1093/chemle/upae087.
Abstract	Formose reaction facilitates the synthesis of sugars from formaldehyde, yet the valuable sugars constitute only a small portion of the total products. This necessitates the need for a chemical scavenger capable of selectively capturing only valuable sugars. With over 600,000 potential combinations of boronic acid-based scavengers available, pursuing a deductive search approach is unfeasible. This study aims to derive guidelines for designing scavengers that readily bind with target sugars while avoiding nontarget ones via machine learning informed by density functional theory calculations.
Publisher	Chemical Society of Japan
Source Title	Chemistry Letters
Volume (Issue)	53(6)
Page	upae087
Date of Issued	2024-06-01
Language	English
Handle URL	https://hdl.handle.net/11094/97753
PISSN	03667022
eISSN	13480715
Relation.isVersionOf	DOI (Publisher Version) https://doi.org/10.1093/chemle/upae087
Access Rights	open access
oaire:version	Accepted Manuscript
Category	学術雑誌論文 Journal Article

Resource Type	journal article
Local Type	Journal Article
DCTERMS.bibliographicCitation	Chemistry Letters. 2024, 53(6), p. upae087
DC.title	Developing a design guideline of boronic acid derivatives to scavenge targeted sugars in the formose reaction products using DFT-based machine learning
DC.creator	Ishihara, Nanako
	Chikatani, Genta
	Nishijima, Hiroaki
	Tabata, Hiro
	Hase, Yoko
	Mukouyama, Yoshiharu
	Nakanishi, Shuji
	Mukaida, Shiho
DC.publisher	Chemical Society of Japan
DC.language' scheme='DCTERMS.RFC1766	English
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2024-06-01
DC.identifier	https://doi.org/10.1093/chemle/upae087
DC.subject	DFT calculation
	formose reaction
	machine learning
DC.description	This is a pre-copyedited, author-produced version of an article accepted for publication in Chemistry Letters following peer review. The version of record Ishihara N., Chikatani G., Nishijima H., et al. Developing a design guideline of boronic acid derivatives to scavenge targeted sugars in the formose reaction products using DFT-based machine learning. Chemistry Letters 53, upae087 (2024) is available online at: https://doi.org/10.1093/chemle/upae087.
DCTERMS.abstract	Formose reaction facilitates the synthesis of sugars from formaldehyde, yet the valuable sugars constitute only a small portion of the total products. This necessitates the need for a chemical scavenger capable of selectively capturing only valuable sugars. With over 600,000 potential combinations of boronic acid-based scavengers available, pursuing a deductive search approach is unfeasible. This study aims to derive guidelines for designing scavengers that readily bind with target sugars while avoiding nontarget ones via machine learning informed by density functional theory calculations.
DC.format	97753
citation_title	Developing a design guideline of boronic acid derivatives to scavenge targeted sugars in the formose reaction products using DFT-based machine learning
citation_author	Ishihara, Nanako
	Chikatani, Genta
	Nishijima, Hiroaki
	Tabata, Hiro
	Hase, Yoko
	Mukouyama, Yoshiharu
	Nakanishi, Shuji
	Mukaida, Shiho
citation_publisher	Chemical Society of Japan
citation_language	English
citation_date	2024-06-01
citation_journal_title	Chemistry Letters
citation_volume	53
citation_issue	6
citation_firstpage	upae087
citation_doi	https://doi.org/10.1093/chemle/upae087
citation_public_url	https://hdl.handle.net/11094/97753

Developing a design guideline of boronic acid derivatives to scavenge targeted sugars in the formose reaction products using DFT-based machine learning

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