In this study we developed a few machine learning models capable of predicting IC50 of small molecules inhibiting TBK1, a promising therapeutic target for the development of drugs modulating immune responses and treating wide range of diseases. We used the CAS Content Collection to assemble a training set of 1,183 chemical structures with experimentally measured IC50 toward TBK1. We explored several machine learning techniques combined with various molecular descriptors to derive and select the best TBK1 inhibitor QSAR models. Several structural alerts responsible for the mechanism of inhibition of TBK1 are also outlined and discussed. In the context of fragment-based drug design, such structural alerts can help identify promising fragments and predict potential liabilities, in order to guide lead optimization in drug discovery.