A quick glance at the results in Table reveals that while the statistical criteria used to validate the models meet or exceed the common standards for good predictors, R 2 of 0.69 for the predictions of the independent external set with the regression model, although acceptable, is noticeable lower than the R 2 of the holdout test set (0.822). The lower value of R 2 for the external set can be attributed to the fact that these structures are external in the true sense of the word. While the chemicals in the holdout test set are external for the model building procedure, they are not entirely external for the model building data with respect to structural similarity. While most of the chemicals in the initial training set originate from different studies, however, a single study often has series of chemical compounds that may bear some structural similarities. In contrast, the TBK1 inhibitors in our independent external test set are not part of the initial set and any similarities that might exist can be attributed to chance. With much more chemical diversity for the classification models where the initial set is 1,183 chemicals strong, both the binary and 3category predictors perform well in the validation of the holdout set as well as the predictions of the independent external set. One particularly important aspect of QSAR modeling is defining the applicability domain 105, 107-111 of the predictors. Or in other words to determine the chemical space where the models are suitable to make quality predictions and avoid a potential misuse of the results. Thus, the predictions for new molecules obtained from a QSAR model are acceptable only if the new molecules fall inside the applicability domain of that model. The applicability domain is the chemical space defined by all molecular descriptors used to build the QSAR model. For a fragment-based methodology the molecular descriptors are the structural alerts obtained by splitting the chemical structures of the training set into all possible sub-fragments. In the current study our algorithm generated over 66K unique fragments from the compounds in the training set which define the domain of applicability for our TBK1 predictive models.