Compared with conventional pKa determination by experimental methods, computational models may provide alternative, universal, and affordable approaches. Quantum-mechanics (QM) based methods can predict pKa yet with significant error (1.28 pKa units as mean absolute error -MAEfor the method by Liao and Nicklaus). More recently, other models based on QM calculations have been developed to further improve the accuracy of the prediction which reached an overall prediction error below 1.0 pKa unit. However, QM based predictions still require considerable computational times and resources which stem their applications to high-throughput molecular discoveries. Moreover, a failure to search the accurate conformation may lead to substantial errors for conformationally sensitive structures, thus more expensive molecular dynamics (MD) methods are commonly required in QM based approaches. Recently, new methods such as the Conformer-Rotamer Ensemble Sampling Tool (CREST) have employed accurate semiempirical QM calculations, specifically using GFN2-xTB. This method allows for faster yet accurate energy computations of the conformational search for various tautomers. On a single CPU core, this search requires seconds per molecule on average. Whereas this represents a significant improvement in speed due to recent computational advancements, the authors of the latest pKa predictor model QupKake emphasize the need for a faster replacement for these semiempirical calculations. Molecular mechanics (MM) based MD methods have also been studied to further accelerate computational prediction of pKa values. In order to estimate the pKa of proteins and large biological compounds, researchers have focused on developing empirical methods and methods based on computed dielectric constants. These approaches could get most of the results within 1.0 pKa unit error with relative small data sets (300-500), although, once more, with a fairly low throughput.