High Througput pKa Prediction Using Semi Empirical Methods

Abstract

A large proportion of organic molecules relevant to medicine and biotechnology contain one or more ionizable groups, which means that fundamental physical and chemical properties (e.g. the charge of the molecule) depend on the pH of the surroundings via the corresponding pKa values of the molecules. As drug- and material design increasingly is being done through high throughput screens, fast - yet accurate - computational pKa prediction methods are becoming crucial to the design process. Current empirical pKa predictors are increasingly found to fail because they are being applied to parts of chemical space for which experimental parameterization data is lacking. We propose to develop a pKa predictor that, due its quantum mechanical foundation, is more generally applicable but still fast enough to be used in high throughput screening. The method has the potential to impact virtually any biotechnological design process involving organic molecules as we will demonstrate for metabolic engineering and organic battery design.