There are many different methods and tools for pK a prediction, and a variety can be seen in this special issue on SAMPL6 results. These techniques vary dramatically in scope, computational cost, and accuracy. Historically, a common approach for predicting pK a was through linear free energy relationships using empirically determined constants to relate an acid or base to a parent molecule in a known database . A related technique, quantitative structure-property relationships (QSPR), remain popular. These incorporate a variety of molecular and atomistic descriptors . Some of these techniques have been updated to use more advanced machine learning models such as artificial neural networks . A variety of quantum mechanical descriptors, including partial atomic charges, have also been shown to be promising in QSPR models -due to computational cost, these methods are impractical for a general model and have only been applied to specific types of ionizable groups . Quantum mechanical calculations from first principles can also be used to calculate pK a using a thermodynamic cycle of deprotonation in the gas phase and the hydration free energy of both the protonated and deprotonated molecule . QM calculations are often still limited in accuracy due to the difficulty in calculating hydration free energies of ionized molecules in implicit solvents. The most successful quantum mechanical predictions from first principles also apply an empirical linear correction factor .