Here, we introduce a new machine learning model for predicting microscopic and macroscopic pK a s. Our goal was to create a universal model which provides predictions that come with accurate estimates of their uncertainty. Most general methods for pK a prediction build separate models for each type of ionizable group. Even predictions based on DFT calculations for model input can require specialized models for different ionizable groups In contrast, we set out to build a single general model which could predict a microscopic pK a for any identified ionizable group. We believe that if our features, or input into the machine learning model, are based on the underlying physical and chemical properties responsible for the variation in deprotonation energy, only one model would be necessary. Artificial neural networks have been successful for predicting pK a , but require substantial training data. We were interested in a machine learning model that could be built from less training data, but did not require an assumption about the shape of the function being fit. Gaussian process regression meets these requirements providing a model based on distributions in feature space. It also automatically incorporates an assessment of uncertainty based on how similar input data is to the training data . Here, we present this new model and our results for the type I and type III components of the SAMPL6 blind challenge.