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Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source

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Predictive design of crystallographic chiral separation.

Rokas Elijošius¹, Emma King-Smith², Felix A Faber¹

¹Department of Physics, University of Cambridge, Cambridge, UK.

Nature Communications

|August 26, 2025

Summary

This study introduces a machine learning model to predict resolving agents for chiral molecule separation, improving efficiency by four to six-fold. The approach accelerates pharmaceutical manufacturing and reduces costs associated with chiral resolutions.

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Area of Science:

Chemical Engineering
Computational Chemistry
Machine Learning

Background:

Efficient separation of chiral molecules is crucial for pharmaceuticals and materials science.
Traditional methods rely on time-consuming trial-and-error processes.
Developing predictive models for chiral resolution is a significant scientific challenge.

Purpose of the Study:

To develop a machine learning-based approach for predicting resolving agents for chiral molecules.
To improve the efficiency and reduce the cost of chiral resolution processes.
To provide a publicly accessible dataset for advancing research in chiral separations.

Main Methods:

Utilized a transformer-based neural network combined with a physics-based representation.
Trained the model on a proprietary dataset of over 6000 chiral resolution experiments.
Validated the model through retrospective testing and prospective experiments on unseen racemates.

Main Results:

Achieved a four to six-fold improvement in hit rate compared to historical methods.
Successfully resolved three out of six unseen racemates in a single experimental round.
Demonstrated an 8-to-1 true positive to false negative ratio in prospective validation.

Conclusions:

The developed machine learning approach significantly enhances the prediction of resolving agents for chiral molecules.
This method offers a faster, more cost-effective alternative to traditional trial-and-error techniques.
The release of the large-scale dataset and the predictive model will accelerate future research and development in chiral resolutions.