It is well established that unintended off-target activity may interfere in multiple biological processes, inducing unwanted side effects. In this context, computational approaches, such as machine learning, can be used to predict the potential for an approved drug to interact with off-targets and identify potential safety-related concerns. For example, previously unknown drug-target interactions for the approved compound Celecoxib were identified, supporting the biological plausibility of reported cardiovascular adverse drug events. Off-target profiling is frequently used to identify candidate drugs for repurposing. For example, computational studies identified baricitinib as a promising JAK inhibitor for repurposing in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 or COVID-19). Baricitinib was considered to be a therapeutic option based on the high affinity for AP-2 associated protein kinase 1 (AAK1), which is key in regulating viral endocytosis and its inhibition may reduce the ability of the virus to infect lung cells. In light of the currently unexplained thrombotic and viral infection risk, and the previously observed off-target binding potential of baricitinib, we sought to identify if the thrombosis and viral infection/reactivation risk may be a result of an off-target effect.