Journal article
Toward machine learning-enhanced high-throughput experimentation for chemistry
- Abstract:
- High-throughput experimentation in chemistry allows for quick and automated exploration of chemical space to, for example, discover new drugs. Combining machine learning techniques with high-throughput experimentation has the potential to speed up and improve chemical space exploration and optimization.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 186.3KB, Terms of use)
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- Publisher copy:
- 10.1016/j.patter.2021.100221
Authors
- Publisher:
- Cell Press
- Journal:
- Patterns More from this journal
- Volume:
- 2
- Issue:
- 3
- Article number:
- 100221
- Publication date:
- 2021-03-12
- Acceptance date:
- 2021-02-12
- DOI:
- EISSN:
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2666-3899
- Pmid:
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33748798
- Language:
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English
- Keywords:
- Pubs id:
-
1301038
- Local pid:
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pubs:1301038
- Deposit date:
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2023-06-12
- ARK identifier:
Terms of use
- Copyright holder:
- Sarah Callaghan
- Copyright date:
- 2021
- Rights statement:
- Copyright 2021 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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