The Utility of Calculated Proton Affinities in Drug Design: A DFT Study

Abstract

Computer-aided drug design comprises several predictive tools, which can calculate various properties of the candidates under development. Proton affinity (PA) is related to pKa (the negative log of the acid dissociation constant (Ka)) one of the fundamental physical properties of drug candidates, determining their water solubility and thus their pharmacokinetic profile. The following questions therefore emerged: to what extent are PA predictions useful in drug design, and can they be reliably used to derive pKa values? Using density functional theory (DFT), it was established that for violuric acid, with three ionisation groups, the PAs correlate well with the measured pKas (R2 = 0.990). Furthermore, an excellent correlation within the amiloride compound family was achieved (R2 = 0.922). In order to obtain correlations for larger compound collections (n = 210), division into chemical families was necessary: carboxylic acids (R2 = 0.665), phenols (R2 = 0.871), and nitrogen-containing molecules (R2 = 0.742). These linear relationships were used to predict pKa values of 90 drug molecules with known pKas. A total of 48 % of the calculated values were within 1 logarithmic unit of the experimental number, but mainstream empirically based methods easily outperform this approach. The conclusion can therefore be reached that PA values cannot be reliably used for predicting pKa values globally but are useful within chemical families and in the event where a specific tautomer of a drug needs to be identified.