Kinases are enzymes that catalyze the transfer of a phosphate group from a high-energy molecule, such as adenosine triphosphate (ATP), to a specific substrate molecule, typically a protein, lipid, or carbohydrate. This process is known as phosphorylation and plays a crucial role in cellular signaling pathways, regulating various cellular functions including metabolism, growth, differentiation, and cell death. Kinases are essential for transmitting signals within cells and coordinating cellular responses to extracellular stimuli. They are involved in a wide range of physiological processes and are often targets for drug development in treating diseases such as cancer and inflammatory disorders. Tank-binding kinase 1 (TBK1) is one such enzyme with kinase activity. Encoded by the TBK1 gene in humans, it is a pivotal serine/threonine kinase that orchestrates a variety of critical cellular processes, including innate immunity, inflammation, autophagy, and cell survival/proliferation. Since its discovery, TBK1 has emerged as a central node in the signaling pathways that underpin the defense mechanisms of the body against pathogens and maintain cellular homeostasis. Its ability to phosphorylate and activate key transcription factors, such as interferon regulatory factors (IRFs) and nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), underscores its essential role in immune responses. The involvement of TBK1 in immune signaling begins with its activation by pattern recognition receptors (PRRs), which detect pathogen-associated molecular patterns (PAMPs) and damageassociated molecular patterns (DAMPs). Upon activation, TBK1 phosphorylates downstream effectors to induce the expression of type I interferons and pro-inflammatory cytokines, which are crucial for antiviral defense and the modulation of inflammatory responses. Furthermore, TBK1 is integral to the autophagy pathway, where it regulates the selective degradation of ubiquitinated proteins and damaged organelles, thereby contributing to cellular quality control and stress responses. The significance of TBK1 extends beyond normal physiological functions; its dysregulation is implicated in a range of pathological conditions. Mutations and altered expression of TBK1 have been associated with neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), highlighting its role in neuronal homeostasis. Additionally, TBK1's involvement in oncogenic pathways links it to cancer progression and survival, making it a potential target for therapeutic intervention. Given its central role in multiple signaling pathways, TBK1 represents a critical juncture in the regulation of immune responses, inflammation, and cell survival. This paper aims to provide a comprehensive overview of TBK1's structural features, its regulatory mechanisms, and its diverse functional roles in health and disease. By elucidating the molecular intricacies of TBK1, we can better understand its contributions to cellular homeostasis and its potential as a therapeutic target in various disease contexts. Its intricate functions make it an intriguing target for further research and therapeutic interventions. The objective of this article is to utilize machine learning approach for developing valuable predictive models for TBK1 inhibition -a topic of utmost importance as an attractive target for drug development. We explored data from the CAS Content Collection , the world's largest human expert-curated collection of scientific data, regarding TBK1 and its implication in a large assortment of diseases and disorders. With the demand for developing efficient TBK1 inhibitors outlined, we further focused on developing predictive analytics for TBK1 inhibition, derived from the fragment-functional analysis descriptors. For such 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. Based on data from the CAS Content Collection, we assembled a training set known TBK1 inhibitors whose IC50 values have been determined experimentally. We explored several machine learning techniques combined with various molecular descriptors to derive and select the best TBK1 QSAR models. We also considered various aspects of TBK1 structure and potential inhibitors. Certain significant structural alerts potentially important for TBK1 inhibition have been outlined and discussed. The novelty and merit of the article stem from identifying the most adequate TBK1 QSAR models and subsequent successful development of advanced positive training data to facilitate and enhance drug discovery for an important therapeutic target such as TBK1 inhibitors, based on extensive, wide-ranging set of scientific information provided by the CAS Content Collection.