Property-Driven Cocrystal Discovery via Gaussian Processes and Active Learning

High Resolution Image Download MS PowerPoint Slide Cocrystallization can be used to tune key drug properties, such as aqueous solubility, without altering molecular structure; however, the space of possible coformers is enormous and design rules are empirical. We present a Bayesian optimization framework that couples Gaussian process (GP) classification and regression that can accelerate cocrystal discovery and solubility enhancement. Starting from 6338 literature-derived binary coformer pairs, vector fingerprints that combine 2D structural information (fragment and MQN fingerprints) with low-cost shape and polarity descriptors were engineered for cocrystal prediction. A GP classifier, trained on an actively constructed training set of ∼1000 coformer pairs selected by uncertainty sampling achieves up to 94% accuracy and Matthews correlation coefficient of 0.79 on a test set of >5000 unseen pairs. Property-driven coformer selection was formulated as a Bayesian optimization problem, using a machine learning model as a surrogate for aqueous solubility and Tanimoto-similarity to guide campaigns across several discovery scenarios. In simulations, the framework rapidly identifies highly soluble cocrystals, typically recovering top-5 candidates after fewer than 10 evaluations. Finally, we validate the workflow experimentally with 12 pharmaceutical and pharmaceutical-like compounds, discovering two new cocrystals, resveratrol + praziquantel and purin-6-amine + thiazole-4-carboxylic acid, with markedly enhanced aqueous solubility. These results demonstrate a practical, data-efficient route to Bayesian optimization for cocrystal design.