Stefan P. Schmid, Leon Schlosser, Frank Glorius, Kjell Jorner
{"title":"Catalysing (organo-)catalysis: Trends in the application of machine learning to enantioselective organocatalysis","authors":"Stefan P. Schmid, Leon Schlosser, Frank Glorius, Kjell Jorner","doi":"10.3762/bjoc.20.196","DOIUrl":null,"url":null,"abstract":"<p><font size='+1'><b>Abstract</b></font></p>\n<p>Organocatalysis has established itself as a third pillar of homogeneous catalysis, besides transition metal catalysis and biocatalysis, as its use for enantioselective reactions has gathered significant interest over the last decades. Concurrent to this development, machine learning (ML) has been increasingly applied in the chemical domain to efficiently uncover hidden patterns in data and accelerate scientific discovery. While the uptake of ML in organocatalysis has been comparably slow, the last two decades have showed an increased interest from the community. This review gives an overview of the work in the field of ML in organocatalysis. The review starts by giving a short primer on ML for experimental chemists, before discussing its application for predicting the selectivity of organocatalytic transformations. Subsequently, we review ML employed for privileged catalysts, before focusing on its application for catalyst and reaction design. Concluding, we give our view on current challenges and future directions for this field, drawing inspiration from the application of ML to other scientific domains.</p>\n<p align='center'><img src='https://www.beilstein-journals.org/bjoc/content/figures/1860-5397-20-196-graphical-abstract.png?max-width=550' border='0'/></p>\n<p><i>Beilstein J. Org. Chem.</i> <b>2024,</b> <i>20,</i> 2280–2304. doi:10.3762/bjoc.20.196</p>","PeriodicalId":8756,"journal":{"name":"Beilstein Journal of Organic Chemistry","volume":"53 1","pages":""},"PeriodicalIF":2.2000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Beilstein Journal of Organic Chemistry","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.3762/bjoc.20.196","RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, ORGANIC","Score":null,"Total":0}
引用次数: 0
Abstract
Organocatalysis has established itself as a third pillar of homogeneous catalysis, besides transition metal catalysis and biocatalysis, as its use for enantioselective reactions has gathered significant interest over the last decades. Concurrent to this development, machine learning (ML) has been increasingly applied in the chemical domain to efficiently uncover hidden patterns in data and accelerate scientific discovery. While the uptake of ML in organocatalysis has been comparably slow, the last two decades have showed an increased interest from the community. This review gives an overview of the work in the field of ML in organocatalysis. The review starts by giving a short primer on ML for experimental chemists, before discussing its application for predicting the selectivity of organocatalytic transformations. Subsequently, we review ML employed for privileged catalysts, before focusing on its application for catalyst and reaction design. Concluding, we give our view on current challenges and future directions for this field, drawing inspiration from the application of ML to other scientific domains.
Beilstein J. Org. Chem.2024,20, 2280–2304. doi:10.3762/bjoc.20.196
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.
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