Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis
Artificial intelligence and machine learning have demonstrated their potential role in predictive chemistry and synthetic planning of small molecules; there are at least a few reports of companies employing in silico synthetic planning into their overall approach to accessing target molecules. A dat...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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American Chemical Society (ACS)
2020
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| Online Access: | https://hdl.handle.net/1721.1/125681 |
| _version_ | 1826207193966313472 |
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| author | Struble, Thomas J Jaakkola, Tommi S Green Jr, William H Barzilay, Regina |
| author2 | Massachusetts Institute of Technology. Department of Chemical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Chemical Engineering Struble, Thomas J Jaakkola, Tommi S Green Jr, William H Barzilay, Regina |
| author_sort | Struble, Thomas J |
| collection | MIT |
| description | Artificial intelligence and machine learning have demonstrated their potential role in predictive chemistry and synthetic planning of small molecules; there are at least a few reports of companies employing in silico synthetic planning into their overall approach to accessing target molecules. A data-driven synthesis planning program is one component being developed and evaluated by the Machine Learning for Pharmaceutical Discovery and Synthesis (MLPDS) consortium, comprising MIT and 13 chemical and pharmaceutical company members. Together, we wrote this perspective to share how we think predictive models can be integrated into medicinal chemistry synthesis workflows, how they are currently used within MLPDS member companies, and the outlook for this field. |
| first_indexed | 2024-09-23T13:45:31Z |
| format | Article |
| id | mit-1721.1/125681 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T13:45:31Z |
| publishDate | 2020 |
| publisher | American Chemical Society (ACS) |
| record_format | dspace |
| spelling | mit-1721.1/1256812022-10-01T16:59:23Z Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis Struble, Thomas J Jaakkola, Tommi S Green Jr, William H Barzilay, Regina Massachusetts Institute of Technology. Department of Chemical Engineering Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Artificial intelligence and machine learning have demonstrated their potential role in predictive chemistry and synthetic planning of small molecules; there are at least a few reports of companies employing in silico synthetic planning into their overall approach to accessing target molecules. A data-driven synthesis planning program is one component being developed and evaluated by the Machine Learning for Pharmaceutical Discovery and Synthesis (MLPDS) consortium, comprising MIT and 13 chemical and pharmaceutical company members. Together, we wrote this perspective to share how we think predictive models can be integrated into medicinal chemistry synthesis workflows, how they are currently used within MLPDS member companies, and the outlook for this field. United States. Defense Advanced Research Projects Agency. Make-It Program (Contract ARO W911NF-16-2-0023) 2020-06-05T12:53:21Z 2020-06-05T12:53:21Z 2020-04 2020-05-18T17:08:07Z Article http://purl.org/eprint/type/JournalArticle 1520-4804 0022-2623 https://hdl.handle.net/1721.1/125681 Struble, Thomas J. et al. “Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis” Journal of Medicinal Chemistry, "Artificial Intelligence in Drug Discovery" Special issue, 2020, © 2020 The Author(s) en 10.1021/acs.jmedchem.9b02120 Journal of Medicinal Chemistry Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf American Chemical Society (ACS) ACS |
| spellingShingle | Struble, Thomas J Jaakkola, Tommi S Green Jr, William H Barzilay, Regina Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis |
| title | Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis |
| title_full | Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis |
| title_fullStr | Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis |
| title_full_unstemmed | Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis |
| title_short | Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis |
| title_sort | current and future roles of artificial intelligence in medicinal chemistry synthesis |
| url | https://hdl.handle.net/1721.1/125681 |
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