Mechanochemistry integrates mechanical and chemical phenomena to spawn new modes of reactivity, faster reaction kinetics and the discovery of novel materials, in a solvent-free and environmental manner. This simple, yet efficient technique has become a well-established screening technique to discover pharmaceutical co-crystals - components that incorporate a secondary crystalline structure into the lattice of an active pharmaceutical ingredient (API) to improve its physicochemical properties. Today, predicting the reactivity of solid-state materials under mechanochemical conditions remains a major challenge. Here, we explore various machine learning algorithms and identified XGBoost ideal to accurately predict mechanochemical co-crystallization. The model was trained using 1000 co-crystallization events and 2083 chemical descriptors, revealing fundamental insights about mechanochemistry. The model was implemented to screen secondary crystalline structures against a model API, yielding three new mechanochemically-formed co-crystals. This predictive model will accelerate the discovery of novel pharmaceuticals while its insights aid at developing a more sustainable chemical industry.
- Machine learning
- Solvent-free manufacturing
ASJC Scopus subject areas
- Chemical Engineering(all)
- Computer Science Applications