Chemometric-assisted cocrystallization: supervised pattern recognition for predicting the formation of new functional cocrystals

Owing to their antimicrobial and insecticide properties, the use of natural compounds like essential oils and their active components has proven to be an effective alternative to synthetic chemicals in different fields ranging from drug delivery to agriculture and from nutrition to food preservation. Their limited application due to the high volatility and scarce water solubility can be expanded by using crystal engineering approaches to tune some properties of the active molecule by combining it with a suitable partner molecule (coformer). However, the selection of coformers and the experimental effort required for discovering cocrystals are the bottleneck of cocrystal engineering. This study explores the use of chemometrics to aid the discovery of cocrystals of active ingredients suitable for various applications. Partial Least Squares-Discriminant Analysis is used to discern cocrystals from binary mixtures based on the molecular features of the coformers. For the first time a dataset comprising also failed cocrystallization experiments and a variety of chemically diverse compounds was utilized. The proposed methodology resulted in a successful prediction rate of 85% for the test set in the model validation phase and of 74% for the external validation set.

Automated summary

This study explores the use of chemometrics to aid the discovery of cocrystals of active ingredients suitable for various applications. Partial Least Squares-Discriminant Analysis is used to discern cocrystals from binary mixtures based on the molecular features of the coformers. The proposed methodology resulted in a successful prediction rate of 85% for the test set in the model validation phase and of 74% for the external validation set.