Recent advances in AI-driven pK_a prediction for proteins and small molecules

Advances in machine-learning techniques and the availability of high-quality pK_a databases have promoted the development of AI-driven pK_a predictors. This review surveys recent advances in AI-driven pK_a prediction for both proteins and small molecules, and reveals that methodology has evolved along divergent trajectories for the two molecular classes, giving rise to largely independent lineages. Finally, open challenges in pK_a prediction, including data scarcity, thermodynamic consistency, and a general-purpose model, are highlighted for future development.