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Grzybowski, Bartosz A.
School of Natural Science
Research Interests
  • Nano science
  • Nanomaterials
  • Programmable Reactions
  • Chemical Networks
  • Cellular Dynamics

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Scaffold-Directed Face Selectivity Machine-Learned from Vectors of Non-covalent Interactions

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Title
Scaffold-Directed Face Selectivity Machine-Learned from Vectors of Non-covalent Interactions
Author
Moskal, MartynaBeker, WiktorSzymkuc, SaraGrzybowski, Bartosz A.
Issue Date
2021-07
Publisher
WILEY-V C H VERLAG GMBH
Citation
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION, v.60, no.28, pp.15230 - 15235
Abstract
This work describes a method to vectorize and Machine-Learn, ML, non-covalent interactions responsible for scaffold-directed reactions important in synthetic chemistry. Models trained on this representation predict correct face of approach in ca. 90 % of Michael additions or Diels-Alder cycloadditions. These accuracies are significantly higher than those based on traditional ML descriptors, energetic calculations, or intuition of experienced synthetic chemists. Our results also emphasize the importance of ML models being provided with relevant mechanistic knowledge; without such knowledge, these models cannot easily "transfer-learn" and extrapolate to previously unseen reaction mechanisms.
URI
https://scholarworks.unist.ac.kr/handle/201301/55377
URL
https://onlinelibrary.wiley.com/doi/10.1002/anie.202101986
DOI
10.1002/anie.202101986
ISSN
1433-7851
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CHM_Journal Papers
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