Machine-Learning-Guided Cocrystal Prediction Based on Large Data Base

A machine-learning model trained on the whole Cambridge Structural Database was developed to assist high-throughput cocrystal screening. With only 2D structures taken as inputs, the probability of cocrystal formation is returned for two given molecules. All of the cocrystal records in the CSD were used as positive samples, while negative samples were constructed by randomly combining different molecules into chemical pairs. Our model showed a prediction ability comparable with that of a widely used ab initio method in a head-to-head comparison test. Both experimental and virtual cocrystal screening against captopril were conducted at the same time to further validate the model. Two cocrystals of captopril with L-proline and sarcosine were obtained and characterized by PXRD, DSC, and FT-IR. These two coformers were also successfully predicted by our model. These results suggest that the tool we developed can be used to effectively guide coformer selection in the discovery of new cocrystals.