Predictive design of crystallographic chiral separation

Rokas Elijošius, Emma King-Smith, Felix A. Faber, Louise Bernier, Simon Berritt, William P. Farrell, Xinjun Hou, Jacquelyn L. Klug-McLeod, Jason Mustakis, Neal W. Sach, Qingyi Yang, Roger M. Howard () and Alpha A. Lee ()
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Rokas Elijošius: University of Cambridge
Emma King-Smith: University of Edinburgh
Felix A. Faber: University of Cambridge
Louise Bernier: Pfizer Research & Development
Simon Berritt: Pfizer Research & Development
William P. Farrell: Pfizer Research & Development
Xinjun Hou: Pfizer Research & Development
Jacquelyn L. Klug-McLeod: Pfizer Research & Development
Jason Mustakis: Pfizer Research & Development
Neal W. Sach: Pfizer Research & Development
Qingyi Yang: Pfizer Research & Development
Roger M. Howard: Pfizer Research & Development
Alpha A. Lee: University of Cambridge

Nature Communications, 2025, vol. 16, issue 1, 1-10

Abstract: Abstract The efficient separation of chiral molecules is a fundamental challenge in the manufacture of pharmaceuticals and light-polarising materials. We developed an approach that combines machine learning with a physics-based representation to predict resolving agents for chiral molecules, using a transformer-based neural network. In retrospective tests, our approach reaches a four to six-fold improvement over the historical - trial and error based - hit rate. We further validate the model in a prospective experiment, where we use the model to design a resolution screen for six unseen racemates. We successfully resolved three of the six mixtures in a single round of experiments and obtained an overall 8-to-1 true positive to false negative ratio. Together with this study, we release a previously proprietary dataset of over 6000 resolution experiments, the largest diastereomeric salt crystallisation dataset to date. More broadly, our approach and open crystallisation data lay the foundation for accelerating and reducing the costs of chiral resolutions.

Date: 2025
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DOI: 10.1038/s41467-025-62825-4

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