Computational chemistry is a valuable tool, as it allows for in silico prediction of key parameters of novel compounds, such as pKa. In the framework of computational pKa determination, the literature offers several approaches based on different level of theories, functionals and continuum solvation models. However, correction factors are often used to provide reliable models that adequately predict pKa. In this work, an accurate protocol based on a direct approach is proposed for computing phenols pKa. Importantly, this methodology does not require the use of correction factors or mathematical fitting, making it highly practical, easy to use and fast. Above all, DFT calculations performed in the presence two explicit water molecules using CAM-B3LYP functional with 6-311G+dp basis set and a solvation model based on density (SMD) led to accurate pKa values. In particular, calculations performed on a series of 13 differently substituted phenols provided reliable results, with a mean absolute error of 0.3. Furthermore, the model achieves accurate results with -CN and -NO₂ substituents, which are usually excluded from computational pKa studies, enabling easy and reliable pKa determination in a wide range of phenols.

The selection of a “perfect tool” for the theoretical determination of acid-base dissociation constants (Ka) is still puzzling. Recently, we developed a user-friendly model exploiting CAM-B3LYP for determining pKa with impressive reliability. Herein, a new challenge is faced, examining a panel of functionals belonging to different rungs of the “Jacob’s ladder” organization, which classifies functionals according to their level of theory. Specifically, meta-generalized gradient approximations (GGAs), hybrid-GGAs, and the more complex range-separated hybrid (RSH)-GGAs were investigated in predicting the pKa of differently substituted carboxylic acids. Therefore, CAM-B3LYP, WB97XD, B3PW91, PBE1PBE, PBEPBE and TPSSTPSS were used, with 6-311G+(d,p) as the basis set and the solvation model based on density (SMD). CAM-B3LYP showed the lowest mean absolute error value (MAE = 0.23) with relatively high processing time. PBE1PBE and B3PW91 provided satisfactory predictions (MAE = 0.34 and 0.38, respectively) with moderate computational time cost, while PBEPBE, TPSSTPSS and WB97XD led to unreliable results (MAE > 1). These findings validate the reliability of our model in predicting carboxylic acids pKa, with MAE well below 0.5 units, using a simplistic theoretical level and a low-cost computational approach.