The efficient separation of chiral molecules is a fundamental challenge in the manufacture of pharmaceuticals and light-polarising materials. Here, the authors develop an approach that combines machine learning with a physics-based representation to predict resolving agents for chiral molecules, using a transformer-based neural network.
- Rokas Elijošius
- Emma King-Smith
- Alpha A. Lee
