2023 Fiscal Year Final Research Report

Rationally designed catalysis for the enantioselective activation of alkenes

Research Project

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Project/Area Number	21H01925
Research Category	Grant-in-Aid for Scientific Research (B)
Allocation Type	Single-year Grants
Section	一般
Review Section	Basic Section 33020:Synthetic organic chemistry-related
Research Institution	Hokkaido University
Principal Investigator	List Benjamin 北海道大学, 化学反応創成研究拠点, 特任教授 (80899253)
Co-Investigator(Kenkyū-buntansha)	SIDOROV PAVEL 北海道大学, 化学反応創成研究拠点, 准教授 (30867619) 辻信弥北海道大学, 化学反応創成研究拠点, 特任准教授 (30873575) 長田裕也北海道大学, 化学反応創成研究拠点, 特任准教授 (60512762) GIMADIEV TIMUR 北海道大学, 化学反応創成研究拠点, 博士研究員 (30874838)
Project Period (FY)	2021-04-01 – 2024-03-31
Keywords	Organocatalysis / Machine learning / Computational chemistry / Asymmetric catalysis
Outline of Final Research Achievements	Building upon the original proposal, our focus lies in understanding the behavior of catalysts and constructing a framework for the development of desired alkene activation. In order to gain deeper insights into the behavior of catalysts, we conducted a computational study of IDPi-catalyzed Mukaiyama-aldol reaction (JACS, 2021). To achieve a more efficient working protocol, we have developed a semi-automated seamless platform spanning from screening to machine learning, which incorporates a newly-developed molecular fragment descriptor. This approach has enabled efficient and robust virtual screening. Using training data with moderate selectivities, we have designed and validated new catalysts demonstrating higher selectivities in a challenging asymmetric tetrahydropyran synthesis via enantioselective hydroalkoxylation (ACIE, 2023). We have also achieved to establish the protocol for quantitatively analyzing the pocket sizes of IDPi catalysts (Nature 2024).
Free Research Field	Asymmetric catalysis
Academic Significance and Societal Importance of the Research Achievements	While traditional approaches to optimizing catalytic processes rely on inductive and qualitative assumptions drawn from screening data, our methods provide fast and robust predictions, enabling the optimization of various catalytic reactions beyond the screening data.