| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2026-01-30 |
| タイトル |
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タイトル |
Inverse ligand design: a generative data-driven model for optimizing vanadyl-based epoxidation catalysts |
| 言語 |
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言語 |
eng |
| キーワード |
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主題Scheme |
Other |
|
主題 |
Inverse design |
| キーワード |
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主題Scheme |
Other |
|
主題 |
Epoxidation reactions |
| キーワード |
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主題Scheme |
Other |
|
主題 |
Generative machine learning |
| キーワード |
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主題Scheme |
Other |
|
主題 |
Vanadyl-based catalysts |
| キーワード |
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主題Scheme |
Other |
|
主題 |
Synthetic accessibility |
| 資源タイプ |
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資源タイプ |
journal article |
| アクセス権 |
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アクセス権 |
open access |
| 著者 |
Ferraz-Caetano, José
Teixeira, Filipe
Cordeiro, M. Natália D.S.
宮尾, 知幸
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| 抄録 |
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内容記述タイプ |
Abstract |
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内容記述 |
We present a machine learning (ML) model for the inverse, de novo generative design of vanadyl-based catalyst ligands for the epoxidation of small alkenes and alcohols. Leveraging molecular descriptors calculated using the RDKit library, our model achieves high performance in validity (64.7 %), uniqueness (89.6 %), and RDKit similarity (91.8 %) after training on a curated dataset of six million structures. Focusing on the modular nature of vanadyl catalyst scaffolds – VOSO4, VO(OiPr)3 and VO(acac)2 – the model generates feasible ligands optimized for catalytic performance. The VOSO4 ligands were consistent with high-yield reactions, while VO(OiPr)3 and VO(acac)2 revealed greater structure variability. Unlike conventional generative approaches, the ligand inverse design also aims to co-design the reaction system, including substrate SMILES and reaction conditions. The model framework was investigated using multiple generative model architectures, resulting in the deep-learning transformer as the most powerful, and uncovering clustering patterns in electronic and structural descriptors related to yield predictions. High synthetic accessibility scores supported the feasibility of the generated ligands. Although the experimental dataset lacks modularity and has few negative data, the model compensates this by using structured descriptor encoding and compatibility scoring. This generative ML framework shows a scalable approach to catalyst ligand design, paving the way for efficient data optimization in synthetic chemistry. |
| 書誌情報 |
en : Journal of Catalysis
巻 453,
p. 1-14,
ページ数 14,
発行日 2025-11-15
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| 出版者 |
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出版者 |
Elsevier |
| ISSN |
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収録物識別子タイプ |
EISSN |
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収録物識別子 |
1090-2694 |
| 出版者版DOI |
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関連タイプ |
isReplacedBy |
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識別子タイプ |
DOI |
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関連識別子 |
https://doi.org/10.1016/j.jcat.2025.116537 |
| 出版者版URI |
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関連タイプ |
isReplacedBy |
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識別子タイプ |
URI |
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関連識別子 |
https://www.sciencedirect.com/science/article/pii/S0021951725006037 |
| 権利 |
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権利情報Resource |
https://creativecommons.org/licenses/by/4.0/ |
|
権利情報 |
© 2025 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
| 著者版フラグ |
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出版タイプ |
NA |
| 助成情報 |
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助成機関名 |
FCT/MECI |
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研究課題番号 |
UID/50006/2025 |
| 助成情報 |
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助成機関名 |
CQ-UM |
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研究課題番号 |
UID/00686/2025 |
