Rationally designed catalysis for the enantioselective activation of alkenes
合理设计的烯烃对映选择性活化催化
基本信息
- 批准号:21H01925
- 负责人:
- 金额:$ 11.23万
- 依托单位:
- 依托单位国家:日本
- 项目类别:Grant-in-Aid for Scientific Research (B)
- 财政年份:2021
- 资助国家:日本
- 起止时间:2021-04-01 至 2024-03-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
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).
基于最初的提议,利用自动合成机器人和机器学习开发快速而稳健的筛选平台是我们今年努力的重点。传统的催化过程优化方法依赖于从筛选数据得出的归纳和定性假设,而机器学习模型的最新发展为数据提供了更定量的评估。然而,由于所需的量子化学计算,这些模型可能会很昂贵。为了避免这些成本,可以使用表示一般结构特征的2D描述符,例如片段计数或二进制指纹。尽管二进制指纹描述符是可访问的且具有成本效益,但它们的预测性能一直受到限制。为了解决这个问题,我们开发了一个机器学习模型,它使用片段描述符,针对不对称催化进行了微调,并表示环芳烃或多芳烃,这使得高效和稳健的虚拟筛选成为可能。使用具有中等选择性的训练数据,我们设计并验证了在具有挑战性的不对称四氢吡喃合成中表现出更高选择性的新型催化剂。这项工作的细节可以在我们的出版物《Angew》上找到。化学。内部艾德(10.1002/anie.202218659)。
项目成果
期刊论文数量(9)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Predicting Highly Enantioselective Catalysts Using Tunable Fragment Descriptors**
使用可调片段描述符预测高度对映选择性催化剂**
- DOI:10.1002/anie.202218659
- 发表时间:2023
- 期刊:
- 影响因子:0
- 作者:Tsuji Nobuya;Sidorov Pavel;Zhu Chendan;Nagata Yuuya;Gimadiev Timur;Varnek Alexandre;List Benjamin
- 通讯作者:List Benjamin
Strong and Confined Acids: Universal Catalysts for Selective Synthesis?
强酸和受限酸:选择性合成的通用催化剂?
- DOI:
- 发表时间:2022
- 期刊:
- 影响因子:0
- 作者:Kubota Koji;Baba Emiru;Seo Tamae;Ishiyama Tatsuo;Ito Hajime;Takashi KUBO;Benjamin List
- 通讯作者:Benjamin List
ASYMMETRIC ORGANOCATALYSIS
- DOI:10.53879/id.58.10.p0005
- 发表时间:2021-12
- 期刊:
- 影响因子:0
- 作者:N. Rao
- 通讯作者:N. Rao
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