Case studies. Overall, a good agreement is found with the MD simulations for most thermodynamic terms (Fig. , and Fig. and Fig. -S14 for more details), including the total free energy (∆G r 2 = 0.743, 1.39 kcal/mol RMSD). More in detail, r 2 for ∆G ranged from 0.67 to 0.92 for 8 of the 11 systems considered, with RMSD values ranging from 0.61 to 2.18 kcal/mol. Among all terms, E sw and ∆G are the most accurately predicted. In 9 out of 11 systems, E sw r 2 values were ≥ 0.70, with the exception of two systems in which it was lower than that (≥ 0.63). Similarly, 7 out of 11 systems showed RMSD values below 2.0 kcal/mol. A very similar trend is found also for ∆G for both r 2 and RMSD. On contrary, the E ww term shows the biggest divergence between WaterKit and MD simulations, with poor r 2 (< 0.5) for 9 systems, except for kith and ital. These errors were mitigated by RMSD always smaller than 1.0 kcal/mol. Similarly small RMSD errors were found for the entropy terms (TS trans and TS orient ), but associated to nearly systematically better r 2 values. As previously noted, the results suggest that significantly smaller range of energies for E ww , TS trans and TS orient compared to E sw is a general rule. low RMSD values for E ww , and entropic terms (TS trans and TS orient ) is also observed for each system studied. The data gathered in the analysis shows that E sw energy prediction accuracy is highly correlated to the ∆G performance accuracy. Given the relatively small contribution from the absolute values of the other terms, the data suggests that an accurate estimate of E sw might be sufficient to identify and characterize strongly bound water molecules. A detailed analysis on individual system shows that Wa-terKit predictions can diverge significantly from MD simulations in a some cases. Divergences are observed for proteins kith (∆G RMSD 2.18 kcal/mol), nram (∆G RSMSD 1.90 kcal/mol), fabp4 (∆G RMSD 2.02 kcal/mol) and hivpr (∆G RMSD 1.83 kcal for 1hpx structure and r 2 0.54 for structure 2zye). Similarly, the E sw RMSD for those systems are about 2.21 to 2.78 for fabp4, hivpr (1hpx), nram and kith, showing again that inaccuracy in ∆G is mainly driven by E sw inaccuracies. In term of water placement prediction, results indicate that WaterKit is able to find the most favorable hydration sites (E sw ≤ 5 kcal/mol) present in the MD simulations with high precision (PPV) and only a minimal number of false positive. However, not all the favorable hydration sites present in MD simulations are found by WaterKit (Fig. ). When considering only favorable hydration sites (≤ -5 kcal/mol), the av-erage sensitivity (TPR) and precision (PPV) are 0.47 ± 0.21 and 0.89 ± 0.24, respectively. The average precision goes down to 0.60 ± 0.16, when considering hydration sites with an Esw ≤ -2 kcal/mol and to 0.58 ± 0.07, when all hydration sites are included. The average sensitivity is less influenced by the energy cutoff, and oscillates between 0.47 and 0.56. The analysis of individual systems provided useful information on the performance of WaterKit with respect to MD simulations. All simulations were performed in absence of bound ligands, which have been used afterwards to provide insight on the interpretation of the results for their possible use for drug design. For all systems, hydration sites were identified using an oxygen density gO ≥ 0.1 of bulk density, and the match of WaterKit results with MD results was evaluated using the strictest cutoff (1.0 Å).