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

Advances in machine-learning techniques and the availability of high-quality pKa databases have promoted the development of AI-driven pKa predictors. This review surveys recent advances in AI-driven pKa 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 pKa prediction, including data scarcity, thermodynamic consistency, and a general-purpose model, are highlighted for future development.