Premium
Scaffold‐Directed Face Selectivity Machine‐Learned from Vectors of Non‐covalent Interactions
Author(s) -
Moskal Martyna,
Beker Wiktor,
Szymkuć Sara,
Grzybowski Bartosz A.
Publication year - 2021
Publication title -
angewandte chemie international edition
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.831
H-Index - 550
eISSN - 1521-3773
pISSN - 1433-7851
DOI - 10.1002/anie.202101986
Subject(s) - intuition , scaffold , computer science , artificial intelligence , machine learning , covalent bond , selectivity , chemistry , cognitive science , psychology , programming language , organic chemistry , catalysis
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.