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

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 cycloadditio...

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Bibliographic Details
Published in:Angewandte Chemie 2021-07, Vol.133 (28), p.15358-15363
Main Authors: Moskal, Martyna, Beker, Wiktor, Szymkuć, Sara, Grzybowski, Bartosz A.
Format: Article
Language:English
Subjects:
Chemistry
Chemists
computer-aided synthesis
machine learning
Reaction mechanisms
Scaffolds
Selectivity
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Summary: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. A machine‐learning, ML, model based on vectors of transition‐state interatomic contacts can predict face‐selectivity of Michael additions or Diels–Alder cycloadditions more accurately than traditional ML schemes, energetic calculations, or seasoned organic chemists.
ISSN:0044-8249
1521-3757
DOI:10.1002/ange.202101986