The other important step in improving our model will be to augment our training set with additional polyprotic molecules. Currently, the likelihood function in Scikit-learn requires one feature vector for each experimental result. Using this function, we would require a large dataset of experimental microscopic pK a s in order to include polyprotic molecules. Generally speaking, it is easier to acquire experimental macroscopic pK a data. Thus, a preferred approach would be to define a new likelihood function which would take advantage of the analytical relationship between microscopic and macroscopic pK a s and evaluate a set of microscopic predictions with one macroscopic value. We are confident that with this expansion we will have a general model which could predict pK a for molecules with any combination of ionizable groups.