Given the computational efficiency of this empirical method, it presents a compelling approach for large-scale pK a calculations. We found that NEQ and FEP+ could outperform PropKa on the FEP+ dataset (Figure ); however, PropKa still showed strong performance on the full dataset (Figure , S4). Here, while the AUE values for the predictions were small, the correlations also tended to be weaker. This was particularly evident for lysine, where the Pearson correlation coefficient was near zero. For precise discrimination of individual residues and an absolute ordering of pK a values, an MD-based free energy approach may be warranted.