Predicting reaction performance in C–N cross-coupling using machine learning

TLDR

Catalyst selection for C–N cross‑coupling is traditionally guided by trial and error, and adapting reactions to complex pharmaceutical substrates is laborious, but machine learning offers a promising alternative. The authors built a high‑throughput dataset and trained a random‑forest model to predict which palladium catalysts best tolerate isoxazoles in C–N bond formation. The model’s predictions also facilitated investigation of the catalyst inhibition mechanism. Ahneman et al.

Abstract

A guide for catalyst choice in the forest Chemists often discover reactions by applying catalysts to a series of simple compounds. Tweaking those reactions to tolerate more structural complexity in pharmaceutical research is time-consuming. Ahneman et al. report that machine learning can help. Using a high-throughput data set, they trained a random forest algorithm to predict which specific palladium catalysts would best tolerate isoxazoles (cyclic structures with an N–O bond) during C–N bond formation. The predictions also helped to guide analysis of the catalyst inhibition mechanism. Science , this issue p. 186