Scaffold‐Directed Face Selectivity Machine‐Learned from Vectors of Non‐covalent Interactions
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 09, 2021
- Source ID
- 10.1002/ange.202101986
Entities
People
- Bartosz Andrzej Grzybowski
- Martyna Moskal
- Sara Szymkuć
- Wiktor Beker
Organizations
- Defense Advanced Research Projects Agency
- Polish Academy of Sciences
- Ulsan National Institute of Science and Technology