Emerging trends in the optimization of organic synthesis through high-throughput tools and machine learning
The discovery of the optimal conditions for chemical reactions is a labor-intensive, time-consuming task that requires exploring a high-dimensional parametric space. Historically, the optimization of chemical reactions has been performed by manual experimentation guided by human intuition and throug...
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| Format: | Article |
| Language: | English |
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Beilstein-Institut
2025-01-01
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| Series: | Beilstein Journal of Organic Chemistry |
| Subjects: | |
| Online Access: | https://doi.org/10.3762/bjoc.21.3 |
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| _version_ | 1832544821543174144 |
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| author | Pablo Quijano Velasco Kedar Hippalgaonkar Balamurugan Ramalingam |
| author_facet | Pablo Quijano Velasco Kedar Hippalgaonkar Balamurugan Ramalingam |
| author_sort | Pablo Quijano Velasco |
| collection | DOAJ |
| description | The discovery of the optimal conditions for chemical reactions is a labor-intensive, time-consuming task that requires exploring a high-dimensional parametric space. Historically, the optimization of chemical reactions has been performed by manual experimentation guided by human intuition and through the design of experiments where reaction variables are modified one at a time to find the optimal conditions for a specific reaction outcome. Recently, a paradigm change in chemical reaction optimization has been enabled by advances in lab automation and the introduction of machine learning algorithms. Therein, multiple reaction variables can be synchronously optimized to obtain the optimal reaction conditions, requiring a shorter experimentation time and minimal human intervention. Herein, we review the currently used state-of-the-art high-throughput automated chemical reaction platforms and machine learning algorithms that drive the optimization of chemical reactions, highlighting the limitations and future opportunities of this new field of research. |
| format | Article |
| id | doaj-art-69a3ff4ed9434e599a183ddbc765e83b |
| institution | Kabale University |
| issn | 1860-5397 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Beilstein-Institut |
| record_format | Article |
| series | Beilstein Journal of Organic Chemistry |
| spelling | doaj-art-69a3ff4ed9434e599a183ddbc765e83b2025-02-03T09:10:17ZengBeilstein-InstitutBeilstein Journal of Organic Chemistry1860-53972025-01-01211103810.3762/bjoc.21.31860-5397-21-3Emerging trends in the optimization of organic synthesis through high-throughput tools and machine learningPablo Quijano Velasco0Kedar Hippalgaonkar1Balamurugan Ramalingam2Institute of Materials Research and Engineering (IMRE), Agency for Science Technology and Research (A*STAR), 2 Fusionopolis Way, Singapore 138634, Republic of Singapore Institute of Materials Research and Engineering (IMRE), Agency for Science Technology and Research (A*STAR), 2 Fusionopolis Way, Singapore 138634, Republic of Singapore Institute of Materials Research and Engineering (IMRE), Agency for Science Technology and Research (A*STAR), 2 Fusionopolis Way, Singapore 138634, Republic of Singapore The discovery of the optimal conditions for chemical reactions is a labor-intensive, time-consuming task that requires exploring a high-dimensional parametric space. Historically, the optimization of chemical reactions has been performed by manual experimentation guided by human intuition and through the design of experiments where reaction variables are modified one at a time to find the optimal conditions for a specific reaction outcome. Recently, a paradigm change in chemical reaction optimization has been enabled by advances in lab automation and the introduction of machine learning algorithms. Therein, multiple reaction variables can be synchronously optimized to obtain the optimal reaction conditions, requiring a shorter experimentation time and minimal human intervention. Herein, we review the currently used state-of-the-art high-throughput automated chemical reaction platforms and machine learning algorithms that drive the optimization of chemical reactions, highlighting the limitations and future opportunities of this new field of research.https://doi.org/10.3762/bjoc.21.3autonomous reactorsdata processinghigh-throughput experimentationmachine learningreaction optimization |
| spellingShingle | Pablo Quijano Velasco Kedar Hippalgaonkar Balamurugan Ramalingam Emerging trends in the optimization of organic synthesis through high-throughput tools and machine learning Beilstein Journal of Organic Chemistry autonomous reactors data processing high-throughput experimentation machine learning reaction optimization |
| title | Emerging trends in the optimization of organic synthesis through high-throughput tools and machine learning |
| title_full | Emerging trends in the optimization of organic synthesis through high-throughput tools and machine learning |
| title_fullStr | Emerging trends in the optimization of organic synthesis through high-throughput tools and machine learning |
| title_full_unstemmed | Emerging trends in the optimization of organic synthesis through high-throughput tools and machine learning |
| title_short | Emerging trends in the optimization of organic synthesis through high-throughput tools and machine learning |
| title_sort | emerging trends in the optimization of organic synthesis through high throughput tools and machine learning |
| topic | autonomous reactors data processing high-throughput experimentation machine learning reaction optimization |
| url | https://doi.org/10.3762/bjoc.21.3 |
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