Our main goals in this study were to develop the best possible predictive models for TBK1 inhibitors that could be used by research scientists in their quest to discover effective drugs against wide range of diseases such as cancer, viral infections, and inflammatory disorders. We also made a conscious effort to explain why the models work based on the top molecular descriptors that appear in the models which may provide an invaluable insight into the mechanism of the TBK1 inhibition. To accomplish this, we used CAS Content Collection to extract all available data associated with target protein TBK1 including IC50 and pIC50 values of inhibitors.. After removing all duplicate structures, records without structural information, salts, and mixtures we arrived at our final training set with 1,183 compounds, all single organic chemicals. Upon close examination of the data, the IC50 values of more than half of the structures have been entered as active (IC50 < 0.1uM or IC50 < 0.001uM) or as inactive (IC50 > 10uM) (Figure ). The data distribution of pIC50 of the training set is outlined in Figure . These specifics of the data entry prompted us to pursue 3 distinctive types of predictive models. For the continuous distribution As already mentioned above, only a fraction of the data is presented with their exactly measured concentration. For the development of the regression models, we only used the 475 structures with the exact experimental measurement of IC50 and omit any data entered as active/inactive as they introduce a significant penalty for the accuracy of the regression models. For the classification models we used the entire set of 1,183 chemicals with 349/834 distribution of active/inactive and a breakpoint at pIC50=7 for the binary models and 349/446/388 of active/marginal/inactive and breakpoints at pIC50=7 and 8 for the 3-category models. We explored several different sets of molecular descriptors, as it is not known beforehand what features best correlate with the inhibition of TBK1. In this investigation we used our proprietary CAS fingerprint , the fragments generated by the Structure-Functional Analysis , and the available molecular descriptors in RdKit . A brief summary of all molecular descriptors is given in Table . RdKit -Diagonal elements: atomic mass, Gasteiger charge, Crippen logP, Crippen MR FractionCSP3 RdKit -The fraction of C atoms that are SP3 hybridized Topological descriptors RdKit -Various topological descriptors