TripK a : Accurate and Scalable Acid–Base Dissociation Property Prediction via Triplet Interaction Networks and Physical Knowledge

The acid-base dissociation constant (pKa) characterizes a molecule's tendency to donate or accept protons, thereby fundamentally influencing its physicochemical properties and behavior. Consequently, pKa is pivotal to diverse applications in drug discovery, including virtual screening and drug design. However, accurate pKa prediction remains challenging, due to intricate atomic interactions among ionizable groups and the scarcity of experimental data. To address these, we propose TripKa, a quantum-informed aqueous pKa predictor that employs a triplet interaction network to model complex thermodynamic coupling in multisite protonation equilibria. TripKa is fine-tuned using quantum-level descriptors as training targets, enabling the extraction of physical insights into molecular acid-base dissociation properties from limited high-quality pKa data. Our proposed TripKa outperforms state-of-the-art methods on multiple pKa benchmarks, including the SAMPL6-8 and Novartis data sets, achieving reductions of over 10% in both mean absolute error and root-mean-square error. Beyond pKa prediction, TripKa further serves as a foundational model for diverse molecular properties prediction and interaction modeling tasks, benefiting from its chemically informed pKa-pretraining.