Weighted Averaging Scheme and Local Atomic Descriptor for pKa Prediction Based on Density Functional Theory.

As a continuation of our work on developing a density functional theory-based pKa predictor, we present conceptual improvements to our previously published shell model, which is a hierarchical organization of pKa training sets and which, in principle, covers all chemical space. The improvements concern the way the studied chemical compound is associated with the data points from the training sets. By introducing a new descriptor of the local atomic environment which foregoes dependence on chemical bonding and connectivity, we are able to automatically locate molecules from the training set that are most relevant to the proton dissociation equilibrium under study. This new scheme leads to the prediction of a single pKa value weighted across multiple training sets and thus patches a defect disclosed in the formulation of our previous model. Using the new parametrization approach, the pKa prediction gets rid of outliers reported in previous applications of our approach, eliminates ambiguity in interpreting the results, and improves the overall accuracy. Our new treatment accounts for multiple conformations both on the level of energetics and parametrization. Illustrative results are shown for several types of chemical structures containing guanidine, amidine, amine, and phenol functional groups, and which are representative of practically important large and flexible drug-like molecules. Our method's performance is compared to the performance of other previously published pKa prediction methods. Further possible improvements to the organization of the training sets and the potential application of our new local atomic descriptor to other kinds of parametrizations are discussed.