| 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, 特任助教 (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 |
Granted (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 / Theoretical Chemistry / Synthetic Robot / Chemoinformatics / Machine learning / Theoretical chemistry / Automated synthesis |
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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 Annual Research Achievements |
The development of a fast and robust screening platform using an automated synthesis robot and machine learning is the focus of our efforts this year, based on the original proposal.
While traditional approaches to optimizing catalytic processes rely on inductive and qualitative assumptions drawn from screening data, recent developments in machine learning models offer a more quantitative evaluation of data. However, these models can be expensive due to the required quantum chemical calculations. To avoid these costs, 2D descriptors such as fragment counts or binary fingerprints, which represent general structural features, could be used. Although binary fingerprint descriptors are accessible and cost-effective, their predictive performance has been limited. To overcome this issue, we developed a machine learning model that employs fragment descriptors, fine-tuned for asymmetric catalysis and representing cyclic or polyaromatic hydrocarbons, which enabled efficient and robust virtual screening. Using training data with moderate selectivities, we designed and validated new catalysts that exhibit higher selectivities in a challenging asymmetric tetrahydropyran synthesis.
The details of this work can be found in our publication in Angew. Chem. Int. Ed. (10.1002/anie.202218659).
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| Current Status of Research Progress |
Current Status of Research Progress
1: Research has progressed more than it was originally planned.
Reason
A fast and robust screening method has been developed by a newly developed fragment descriptor. The developed method enabled predicting higher selective catalyst structures from training data comprised of only moderately selective catalysts, without using any quantum calculations. Also, for more efficient screening and data consistency, a synthesis robot was employed, streamlining the process from experiments to data generation.
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| Strategy for Future Research Activity |
Based on the developed method, we will investigate the intermolecular variant. For a rapid expansion of the catalyst library by using automated synthesis, the optimization of the conditions of catalyst synthesis has been performed. In addition, mechanistic studies using computational chemistry are also performed in parallel.
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