Speeding Up the Cocrystallization Process: Machine Learning-Combined Methods for the Prediction of Multicomponent Systems

Abstract

Pharmaceutical cocrystals are crystalline materials composed of at least two molecules, i.e., an active pharmaceutical ingredient (API) and a coformer, assembled by noncovalent forces. Cocrystallization is successfully applied to improve the physicochemical properties of APIs, such as solubility, dissolution profile, pharmacokinetics, and stability. However, choosing the ideal coformer is a challenging task in terms of time, efforts, and laboratory resources. Several computational tools and machine learning (ML) models have been proposed to mitigate this problem. However, the challenge of achieving a robust and generalizable predictive method is still open. In this study, we propose a new approach to quickly predict the formation of cocrystals, employing partial least squares-discriminant analysis, random forest, and neural networks. The models were based on the data sets of 13 structurally different APIs with both positive and negative cocrystallization outcomes. At the same time, the features were specially selected from a variety of molecular descriptors to explain the phenomenon of the cocrystallization. All of the proposed ML models showed a cross-validation accuracy higher than 83%. Furthermore, this approach was successfully applied to drive the cocrystallization experimental tests of 2-phenylpropionic acid, showcasing the high potential of the ML models in practice.

Optimizing Drug Development: Harnessing the Sustainability of Pharmaceutical Cocrystals

“…This will enable the selection of the most suitable and economical approach (for example, cocrystal versus prodrug) to optimize the physicochemical properties of the lead molecules. In this regard, in line with the recent surge in the use of artificial intelligence and data analytics in manufacturing, the successful use of machine learning for prediction of cocrystal formation between pairs of molecules − can significantly ease the experimental burden of cocrystal screening. The use of continuous and solvent-free manufacturing methods in the production of cocrystals has the potential to enhance the ecofriendliness of cocrystallization and continues to be at the forefront of further development.…”

Crystal Structure Landscape of Diarylethene-Based Crystalline Solids: A Comprehensive CSD Analysis

“…As machine learning and artificial intelligence play an increasingly important role in crystal structure prediction and crystal engineering, − it is critical that these tools be provided with high-quality and complete data. The CSD is an unparalleled repository for small-molecule single-crystal data; however, it is reliant upon entries from the scientific community it serves.…”