A transfer learning approach for reaction discovery in small data situations using generative model
Summary: Sustainable practices in chemical sciences can be better realized by adopting interdisciplinary approaches that combine the advantages of machine learning (ML) on the initially acquired small data in reaction discovery. Developing new reactions generally remains heuristic and even time and...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-07-01
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| Series: | iScience |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004222009336 |
| _version_ | 1828517027818504192 |
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| author | Sukriti Singh Raghavan B. Sunoj |
| author_facet | Sukriti Singh Raghavan B. Sunoj |
| author_sort | Sukriti Singh |
| collection | DOAJ |
| description | Summary: Sustainable practices in chemical sciences can be better realized by adopting interdisciplinary approaches that combine the advantages of machine learning (ML) on the initially acquired small data in reaction discovery. Developing new reactions generally remains heuristic and even time and resource intensive. For instance, synthesis of fluorine-containing compounds, which constitute ∼20% of the marketed drugs, relies on deoxyfluorination of abundantly available alcohols. Herein, we demonstrate the use of a recurrent neural network-based deep generative model built on a library of just 37 alcohols for effective learning and exploration of the chemical space. The proof-of-concept ML model is able to generate good quality, synthetically accessible, higher-yielding novel alcohol molecules. This protocol would have superior utility for deployment into a practical reaction discovery pipeline. |
| first_indexed | 2024-12-11T18:35:58Z |
| format | Article |
| id | doaj.art-31a2ed378fd74741a10e82211622f63c |
| institution | Directory Open Access Journal |
| issn | 2589-0042 |
| language | English |
| last_indexed | 2024-12-11T18:35:58Z |
| publishDate | 2022-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj.art-31a2ed378fd74741a10e82211622f63c2022-12-22T00:54:46ZengElsevieriScience2589-00422022-07-01257104661A transfer learning approach for reaction discovery in small data situations using generative modelSukriti Singh0Raghavan B. Sunoj1Department of Chemistry, Indian Institute of Technology Bombay, Mumbai 400076, India; Corresponding authorDepartment of Chemistry, Indian Institute of Technology Bombay, Mumbai 400076, India; Centre for Machine Intelligence and Data Science, Indian Institute of Technology Bombay, Mumbai 400076, India; Corresponding authorSummary: Sustainable practices in chemical sciences can be better realized by adopting interdisciplinary approaches that combine the advantages of machine learning (ML) on the initially acquired small data in reaction discovery. Developing new reactions generally remains heuristic and even time and resource intensive. For instance, synthesis of fluorine-containing compounds, which constitute ∼20% of the marketed drugs, relies on deoxyfluorination of abundantly available alcohols. Herein, we demonstrate the use of a recurrent neural network-based deep generative model built on a library of just 37 alcohols for effective learning and exploration of the chemical space. The proof-of-concept ML model is able to generate good quality, synthetically accessible, higher-yielding novel alcohol molecules. This protocol would have superior utility for deployment into a practical reaction discovery pipeline.http://www.sciencedirect.com/science/article/pii/S2589004222009336Artificial intelligenceComputational chemistryFunctional group chemistryModeling chemical reactivity |
| spellingShingle | Sukriti Singh Raghavan B. Sunoj A transfer learning approach for reaction discovery in small data situations using generative model iScience Artificial intelligence Computational chemistry Functional group chemistry Modeling chemical reactivity |
| title | A transfer learning approach for reaction discovery in small data situations using generative model |
| title_full | A transfer learning approach for reaction discovery in small data situations using generative model |
| title_fullStr | A transfer learning approach for reaction discovery in small data situations using generative model |
| title_full_unstemmed | A transfer learning approach for reaction discovery in small data situations using generative model |
| title_short | A transfer learning approach for reaction discovery in small data situations using generative model |
| title_sort | transfer learning approach for reaction discovery in small data situations using generative model |
| topic | Artificial intelligence Computational chemistry Functional group chemistry Modeling chemical reactivity |
| url | http://www.sciencedirect.com/science/article/pii/S2589004222009336 |
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