Predictive design of crystallographic chiral separation

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. On historical data, our approach is 4-6 times more accurate than current practice. 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 6,000 resolution experiments, the largest diastereomeric salt crystallisation dataset to date. More broadly, our approach and open crystallization data lay the foundation for accelerating and reducing the costs of chiral r..., High-Throughput Experimentation reactions were set up inside of an INERT Inc. triple double sized glove box with O2 and H2O levels  <20 ppm. Glass vials (0.7 mL, 8 x 30 mm) pre-equipped with stir bars were used for each reaction. The reactions were set up with the components and conditions described by each dataset entry at 0.04~mmol scale and 0.2 M final concentration with a 1:1 ratio of acid to base and 200 μL total volume. The reaction vials were sealed by crimp under the glove-box environment and placed in a metal Chemglass Optichem 96-well heating plate atop a general IKA stirrer heater plate with an external temperature probe to accurately and evenly control the plate. The vials were heated for 1 hour at 80 C before being cooled over 3 hours and left to stir for a further 15hrs at 25 C. Solubility observations were made after the hour at 80 C and after the 15hr stir period at 25 C. At the reaction end point, the vials were centrifuged to settle any precipitates and the liq..., , # Data from: Predictive design of crystallographic chiral separation [https://doi.org/10.5061/dryad.d2547d89c](https://doi.org/10.5061/dryad.d2547d89c) Data supporting the manuscript Predictive design of crystallographic chiral separation. We provide the training data, the prospective screen designed with the model, and the raw experimental results of the prospective screen. In addition, we provide the models used for retrospective and prospective experiments, as well as the auto encoders for compressing the representations. ## Description of the data and file structure ### CSV Files #### `ChiralSeparationCleanedWithNoisyDataDisclosable.csv` This file contains the historical data used to train the machine learning models. Each row represents a single diastereomeric resolution experiment. Method to generate exact cross validation splits used in the manuscript can be found in the GitHub repository below. - `notebook_id`: (String) The internal experiment identifier. - `solvent_1`...,