Unlocking the Potential of Machine Learning in Co-crystal Prediction by a Novel Approach Integrating Molecular Thermodynamics.

Co-crystal engineering is of interest for many applications in pharmaceutical, chemical, and materials fields, but rational design of co-crystals is still challenging. Although artificial intelligence has revolutionized decision-making processes in material design, limitations in generalization and mechanistic understanding remain. Herein, we sought to improve prediction of co-crystals by combining mechanistic thermodynamic modeling with machine learning. We constructed a brand-new co-crystal database, integrating drug, coformer, and reaction solvent information. By incorporating various thermodynamic models, the predictive performance was significantly enhanced. Benefiting from the complementarity of thermodynamic mechanisms and structural descriptors, the model coupling three thermodynamic models achieved optimal predictive performance in coformer and solvent screening. The model was rigorously validated against benchmark models using challenging independent test sets, showcasing superior performance in both coformer and solvent predicting with accuracy over 90%. Further, we employed SHAP analysis for model interpretation, suggesting that thermodynamic mechanisms are prominent in the model's decision-making. Proof-of-concept studies on ketoconazole validated the model's efficacy in identifying coformers/solvents, demonstrating its potential in practical application. Overall, our work enhanced the understanding of co-crystallization and highlighted the strategy that integrates mechanistic insights with data-driven models to accelerate the rational design and synthesis of co-crystals, as well as various other functional materials.