We intend in this research to establish a rational method for the development of novel glucocorticoid receptor ligands to more effectively prevent respiratory inflammation. Corticosteroids, a class of steroid hormones, are naturally inclined to bind to the glucocorticoid receptor and, in this research, are the basis for exploring other novel and non-intuitive structures. To be more effective than currently available medications, novel compounds must be highly selective toward the lungs and must be inactivated when exposed to the main circulation, thus preventing the participation of the ligand in other systems and consequently reducing systemic side-effects. We look to use the inverse-quantitative structure-activity relationship algorithm with the Signature molecular descriptor to generate new ligands based upon the structures and activities of 65 experimentally studied corticosteroids. Inverse-quantitative structure-activity relationship explore many possible combinations of atom connectivity while structural filters and other scoring approaches are used to predict and identify the most promising candidates for further study. Properties explored include high receptor binding affinity, high systemic clearance, high plasma protein binding and low oral bioavailability. Among more than 300 million potential candidates generated, 84 high priority compounds with properties predicted to be at least as or more effective than currently available corticosteroids have been identified with this procedure.