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Chemical Graph-Based Transformer Models for Yield Prediction of High-Throughput Cross-Coupling Reaction Datasets
http://hdl.handle.net/10061/0002001043
http://hdl.handle.net/10061/00020010439ea36110-85d2-4d1f-b644-8198292848dc
| アイテムタイプ | 学術雑誌論文 / Journal Article(1) | |||||||||||||||
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| 公開日 | 2025-07-09 | |||||||||||||||
| タイトル | ||||||||||||||||
| タイトル | Chemical Graph-Based Transformer Models for Yield Prediction of High-Throughput Cross-Coupling Reaction Datasets | |||||||||||||||
| 言語 | ||||||||||||||||
| 言語 | eng | |||||||||||||||
| キーワード | ||||||||||||||||
| 主題Scheme | Other | |||||||||||||||
| 主題 | Addition reactions | |||||||||||||||
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| 主題Scheme | Other | |||||||||||||||
| 主題 | Additives | |||||||||||||||
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| 主題Scheme | Other | |||||||||||||||
| 主題 | Aromatic compounds | |||||||||||||||
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| 主題Scheme | Other | |||||||||||||||
| 主題 | Chemical reactions | |||||||||||||||
| キーワード | ||||||||||||||||
| 主題Scheme | Other | |||||||||||||||
| 主題 | Neural networks | |||||||||||||||
| 資源タイプ | ||||||||||||||||
| 資源タイプ | journal article | |||||||||||||||
| アクセス権 | ||||||||||||||||
| アクセス権 | open access | |||||||||||||||
| 著者 |
Sato, Akinori
× Sato, Akinori
× Asahara, Ryosuke
× 宮尾, 知幸
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| 抄録 | ||||||||||||||||
| 内容記述タイプ | Abstract | |||||||||||||||
| 内容記述 | The chemical reaction yield is an important factor to determine the reaction conditions. Recently, many data-driven models for yield prediction using high-throughput experimentation datasets have been reported. In this study, we propose a neural network architecture based on the chemical graphs of the reaction components to predict the reaction yield. The proposed model is the sequential combination of a message-passing neural network and a transformer encoder (MPNN-Transformer). The reaction components are converted to molecular matrices by the first network, followed by the interplay of the reaction components in the second network after adding the embeddings of the compound roles in the chemical reaction. The predictive ability of the proposed models was compared with state-of-the-art yield prediction models using two high-throughput experimental datasets: the Buchwald$2013Hartwig cross-coupling (BHC) and Suzuki$2013Miyaura cross-coupling (SMC) reaction datasets. Overall, the MPNN-Transformer models showed high prediction accuracy for the BHC reaction datasets and some of the extrapolation-oriented SMC reaction datasets. These models also performed well when the training dataset size was relatively large. Furthermore, analyzing the poorly predicted reactions for the BHC reaction dataset revealed a limitation of the data-driven yield prediction approach based on the chemical structural similarity. | |||||||||||||||
| 書誌情報 |
en : ACS Omega 巻 9, 号 39, p. 40907-40919, ページ数 13, 発行日 2024-10-01 |
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| 出版者 | ||||||||||||||||
| 出版者 | American Chemical Society | |||||||||||||||
| ISSN | ||||||||||||||||
| 収録物識別子タイプ | EISSN | |||||||||||||||
| 収録物識別子 | 2470-1343 | |||||||||||||||
| 出版者版DOI | ||||||||||||||||
| 関連タイプ | isReplacedBy | |||||||||||||||
| 識別子タイプ | DOI | |||||||||||||||
| 関連識別子 | https://doi.org/10.1021/acsomega.4c06113 | |||||||||||||||
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| 関連タイプ | isReplacedBy | |||||||||||||||
| 識別子タイプ | URI | |||||||||||||||
| 関連識別子 | https://doi.org/10.1021/acsomega.4c06113 | |||||||||||||||
| 権利 | ||||||||||||||||
| 権利情報Resource | https://creativecommons.org/licenses/by/4.0/ | |||||||||||||||
| 権利情報 | $00A9 2024 The Authors. Published by American Chemical Society. This article is licensed under CC-BY 4.0 | |||||||||||||||
| 著者版フラグ | ||||||||||||||||
| 出版タイプ | NA | |||||||||||||||
| 助成情報 | ||||||||||||||||
| 助成機関名 | Japan Society for the Promotion of Science (JSPS) | |||||||||||||||
| 研究課題番号 | JP21H05220 | |||||||||||||||
| 研究課題名 | 多様な分子構造の自動設計と有機合成反応の新規表現開発計画研究 | |||||||||||||||
