Deriving intuition in catalyst design with machine learning

Catalysts for asymmetric syntheses provide a powerful means to manipulate and obtain matter with defined stereochemistry. In a recent issue of Cell Reports Physical Science, Kanai and colleagues report on a data-driven approach to identify key components in iridium/boron catalysts for the on-demand synthesis of stereochemically defined alpha-allyl carboxylic acids.

Automated summary

In this article, Kanai and colleagues present a data-driven approach to identify key components in iridium/boron catalysts for the on-demand synthesis of stereochemically defined alpha-allyl carboxylic acids. This provides a new perspective for asymmetric syntheses.