| Project/Area Number |
21H01925
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Review Section |
Basic Section 33020:Synthetic organic chemistry-related
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| Research Institution | Hokkaido University |
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Principal Investigator |
List Benjamin 北海道大学, 化学反応創成研究拠点, 特任教授 (80899253)
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| 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)
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| Project Period (FY) |
2021-04-01 – 2024-03-31
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| Project Status |
Completed (Fiscal Year 2023)
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| Budget Amount *help |
¥17,550,000 (Direct Cost: ¥13,500,000、Indirect Cost: ¥4,050,000)
Fiscal Year 2023: ¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
Fiscal Year 2022: ¥7,150,000 (Direct Cost: ¥5,500,000、Indirect Cost: ¥1,650,000)
Fiscal Year 2021: ¥8,060,000 (Direct Cost: ¥6,200,000、Indirect Cost: ¥1,860,000)
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| Keywords | Organocatalysis / Machine learning / Computational chemistry / Asymmetric catalysis / Theoretical chemistry / Automated synthesis / Theoretical Chemistry / Synthetic Robot / Chemoinformatics |
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| Outline of Research at the Start |
The applicants will investigate catalytic enantiocontrol of carbocations generated by the activation of alkenes. In order to achieve this goal, catalysts having higher acidities and modifiable microenvironments will be designed and synthesized. A synthetic robot, theoretical calculations, machine learning would also be employed to accelerate the screening process, leading to a more efficient and rational design of asymmetric catalysis.
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| 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).
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| 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.
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