| Autor: |
Dotson JJ; Department of Chemistry, University of Utah, Salt Lake City, Utah 84112, United States., van Dijk L; Department of Chemistry, University of Utah, Salt Lake City, Utah 84112, United States., Timmerman JC; Department of Small Molecule Process Chemistry, Genentech, Inc., South San Francisco, California 94080, United States., Grosslight S; Department of Chemistry, University of Utah, Salt Lake City, Utah 84112, United States., Walroth RC; Department of Small Molecule Process Chemistry, Genentech, Inc., South San Francisco, California 94080, United States., Gosselin F; Department of Small Molecule Process Chemistry, Genentech, Inc., South San Francisco, California 94080, United States., Püntener K; Synthetic Molecules Technical Development, Process Chemistry & Catalysis, F. Hoffmann-La Roche Limited, CH-4070 Basel, Switzerland., Mack KA; Department of Small Molecule Process Chemistry, Genentech, Inc., South San Francisco, California 94080, United States., Sigman MS; Department of Chemistry, University of Utah, Salt Lake City, Utah 84112, United States. |
| Abstrakt: |
Optimization of the catalyst structure to simultaneously improve multiple reaction objectives (e.g., yield, enantioselectivity, and regioselectivity) remains a formidable challenge. Herein, we describe a machine learning workflow for the multi-objective optimization of catalytic reactions that employ chiral bisphosphine ligands. This was demonstrated through the optimization of two sequential reactions required in the asymmetric synthesis of an active pharmaceutical ingredient. To accomplish this, a density functional theory-derived database of >550 bisphosphine ligands was constructed, and a designer chemical space mapping technique was established. The protocol used classification methods to identify active catalysts, followed by linear regression to model reaction selectivity. This led to the prediction and validation of significantly improved ligands for all reaction outputs, suggesting a general strategy that can be readily implemented for reaction optimizations where performance is controlled by bisphosphine ligands. |
