Abstract Title
Training of Continuum Solvation Model Parameters for Prediction of Molecular Acidity
Abstract
Protons are vital to energy conversion in living cells and fuel cells, ion exchange in biological and synthetic membranes, and catalyzing chemical reactions in proteins and synthetic reactions. Computational methods that allow to predict pKa values of molecules and materials are thus of paramount importance. However, acidity involves charged species in solution. Continuum solvation models have good accuracy for neutral species but struggle with charged species. Here, we present a preliminary parameter search to improve the prediction of pKa values using the Soft Sphere Continuum Solvation (SSCS) model with field aware corrections, a continuum model designed to handle molecular systems as well as materials interfaces. SSCS, as implemented in the Environ Computational Library, was used to characterize solvation free energies for the species involved in the proton-transfer reactions, with the remaining free energy contributions computed in the gas phase. The parameter search was performed over 10 compounds. The results show that there is clearly potential for significantly improving pKa prediction through optimization of the parameters for the field aware corrections.
Training of Continuum Solvation Model Parameters for Prediction of Molecular Acidity
Protons are vital to energy conversion in living cells and fuel cells, ion exchange in biological and synthetic membranes, and catalyzing chemical reactions in proteins and synthetic reactions. Computational methods that allow to predict pKa values of molecules and materials are thus of paramount importance. However, acidity involves charged species in solution. Continuum solvation models have good accuracy for neutral species but struggle with charged species. Here, we present a preliminary parameter search to improve the prediction of pKa values using the Soft Sphere Continuum Solvation (SSCS) model with field aware corrections, a continuum model designed to handle molecular systems as well as materials interfaces. SSCS, as implemented in the Environ Computational Library, was used to characterize solvation free energies for the species involved in the proton-transfer reactions, with the remaining free energy contributions computed in the gas phase. The parameter search was performed over 10 compounds. The results show that there is clearly potential for significantly improving pKa prediction through optimization of the parameters for the field aware corrections.