Figure summarizes the main findings: our NES approach with CHARMM36m-ECC and residue coupling accounted for, significantly exceeds the performance of plain CHARMM36m and two in silico predictors, with an overall average unsigned error (AUE) of 1.61 ± 0.21 pK, compared to 2.69 ± 0.29 pK and 3.08 ± 0.29 pK for PypK a and PropK a , respectively. This stronger performance was also reflected in the Pearson correlation, which was 0.60 ± 0.09 with CHARMM36m-ECC compared to 0.22 ± 0.12 and 0.31 ± 0.14 with PypK a and PropK a , respectively. Considering predictions within a certain tolerance, CHARMM36m-ECC correctly predicts 39 ± 8% of residues within 1 pK and 73±7% within 2 pK, compared to 22±6% and 44±8% with PypK a and 15±6% and 34 ± 7% with PropK a . If the objective is only to identify reactive cysteines (i.e., ∆pK a < 0) rather than assessing their exact pK a s we can consider this as a binary classification problem; performance can then be measured using the Matthews Correlation Coefficient (MCC) . In this regard, CHARMM36m-ECC provided the best performance (Figure ) with a MCC = 0.59, compared to MCC = 0.14 and MCC = 0.27 for PypKa and PropKa, respectively. This final comparison highlights that charge scaling can significantly improve cysteine pK a calculations, pushing the accuracy well above unscaled force fields and conventional predictor methods.