{"title":"Machine learning-guided strategies for reaction conditions design and optimization.","authors":"Lung-Yi Chen, Yi-Pei Li","doi":"10.3762/bjoc.20.212","DOIUrl":null,"url":null,"abstract":"<p><p>This review surveys the recent advances and challenges in predicting and optimizing reaction conditions using machine learning techniques. The paper emphasizes the importance of acquiring and processing large and diverse datasets of chemical reactions, and the use of both global and local models to guide the design of synthetic processes. Global models exploit the information from comprehensive databases to suggest general reaction conditions for new reactions, while local models fine-tune the specific parameters for a given reaction family to improve yield and selectivity. The paper also identifies the current limitations and opportunities in this field, such as the data quality and availability, and the integration of high-throughput experimentation. The paper demonstrates how the combination of chemical engineering, data science, and ML algorithms can enhance the efficiency and effectiveness of reaction conditions design, and enable novel discoveries in synthetic chemistry.</p>","PeriodicalId":8756,"journal":{"name":"Beilstein Journal of Organic Chemistry","volume":null,"pages":null},"PeriodicalIF":2.2000,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11457048/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Beilstein Journal of Organic Chemistry","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.3762/bjoc.20.212","RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"CHEMISTRY, ORGANIC","Score":null,"Total":0}
引用次数: 0
Abstract
This review surveys the recent advances and challenges in predicting and optimizing reaction conditions using machine learning techniques. The paper emphasizes the importance of acquiring and processing large and diverse datasets of chemical reactions, and the use of both global and local models to guide the design of synthetic processes. Global models exploit the information from comprehensive databases to suggest general reaction conditions for new reactions, while local models fine-tune the specific parameters for a given reaction family to improve yield and selectivity. The paper also identifies the current limitations and opportunities in this field, such as the data quality and availability, and the integration of high-throughput experimentation. The paper demonstrates how the combination of chemical engineering, data science, and ML algorithms can enhance the efficiency and effectiveness of reaction conditions design, and enable novel discoveries in synthetic chemistry.
这篇综述探讨了利用机器学习技术预测和优化反应条件方面的最新进展和挑战。论文强调了获取和处理大量不同化学反应数据集的重要性,以及使用全局和局部模型指导合成过程设计的重要性。全局模型利用综合数据库中的信息为新反应提出一般反应条件,而局部模型则对特定反应系列的具体参数进行微调,以提高产率和选择性。论文还指出了该领域目前存在的局限性和机遇,如数据质量和可用性以及高通量实验的整合。论文展示了化学工程、数据科学和 ML 算法的结合如何提高反应条件设计的效率和有效性,以及如何实现合成化学的新发现。
期刊介绍:
The Beilstein Journal of Organic Chemistry is an international, peer-reviewed, Open Access journal. It provides a unique platform for rapid publication without any charges (free for author and reader) – Platinum Open Access. The content is freely accessible 365 days a year to any user worldwide. Articles are available online immediately upon publication and are publicly archived in all major repositories. In addition, it provides a platform for publishing thematic issues (theme-based collections of articles) on topical issues in organic chemistry.
The journal publishes high quality research and reviews in all areas of organic chemistry, including organic synthesis, organic reactions, natural product chemistry, structural investigations, supramolecular chemistry and chemical biology.