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Doyle Lab Uses Machine Learning to Predict Reaction Yields

Research Highlights- - By Liz Fuller-Wright, Office of Communications
Research Area:
Catalysis / Synthesis
Theoretical Chemistry

Abigail Doyle, the A. Barton Hepburn Professor of Chemistry, led a team of researchers from Princeton University and Merck who have developed state-of-the-art software to predict reaction yields while varying up to four components. Their software is designed to work for any reaction on any substrate, making it a powerful tool in expediting the synthesis of new medicines.

The paper, “Predicting reaction performance in C–N cross-coupling using machine learning” by Derek Ahneman, Jesús Estrada, Shishi Lin, Spencer Dreher and Abigail Doyle, was published Feb. 15 in the journal Science. Financial support was provided by Princeton University, an Amgen Young Investigator Award and a Camille-Dreyfus Teacher Scholar Award.

Read the full story here: Chemists harness artificial intelligence to predict the future of chemical reactions