| Project/Area Number |
23K16936
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 61030:Intelligent informatics-related
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| Research Institution | Hokkaido University |
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Principal Investigator |
SIDOROV PAVEL 北海道大学, 化学反応創成研究拠点, 准教授 (30867619)
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| Project Period (FY) |
2023-04-01 – 2025-03-31
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| Project Status |
Granted (Fiscal Year 2023)
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| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2024: ¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
Fiscal Year 2023: ¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
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| Keywords | machine learning / chemoinfrormatics / molecular descriptors / Machine learning / Organic chemistry / Chemical reactions |
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| Outline of Research at the Start |
1) Machine learning requires relevant data, and the data collection will be performed at first from scientific literature and in-house experimentation.
2) Development of novel methodology for machine learning models
3) Validation of models experimentally to search for novel potent catalysts
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| Outline of Annual Research Achievements |
The project is dedicated to the development of novel stereo-sensitive molecular descriptors and their application in modeling the selectivity of catalysts in chemical reactions. During last year, the initial developments of such representations have been undertaken, based on the established chemical libraries. By adding the stereochemical marking into the structure representation, we were able to computationally distinguish compounds of different stereochemistry, which is currently unavailable via other informatics-based libraries. The developments and some applications were published in a journal (Chemistry - A European Journal) and presented in domestic and international conferences (8th Nara School on Chemoinformatics, Nov 2023).
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
Currently, the novel stereo-sensitive representations are undergoing the benchmark on in-house data provided by the collaborators in WPI-ICReDD. Further developments for the refinement of codebase and addition of new features are ongoing.
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| Strategy for Future Research Activity |
This year, the application of the new representation to modeling the selectivity of catalysts will be the main focus of the project. The in-house data obtained from the collaborators in WPI-ICReDD will be used as the center of the study. Mukaiyama reaction is the focus of the dataset and represents a challenge for modeling due to the presence of several stereo centers, which cannot be treated with a simple approach. Furthermore, the type of catalysts that is used in this study requires additional care, as it possesses a special type of stereocenter (axial chirality) that cannot be managed with past approaches. With our new representation, we aim to design a new potent catalyst for this reaction and validate it experimentally as the final goal.
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