The glucocorticoid receptor (GR) is a member of the nuclear receptor superfamily that affects immune response, development, and metabolism in target tissues. Glucocorticoids are widely used to treat diverse pathophysiological conditions, but their clinical applicability is limited by side effects. A prediction of the binding affinity toward the GR would be beneficial for identifying glucocorticoid-mediated adverse effects triggered by drugs or chemicals. By identifying the binding mode to the GR using flexible docking (software Yeti) and quantifying the binding affinity through multidimensional QSAR (software Quasar), we validated a model family based on 110 compounds, representing four different chemical classes. The correlation with the experimental data (cross-validated r(2)=0.702; predictive r(2)=0.719) suggests that our approach is suited for predicting the binding affinity of related compounds toward the GR. After challenging the model by a series of scramble tests, a consensus approach (software Raptor), and a prediction set, it was incorporated into our VirtualToxLab and used to simulate and quantify the interaction of 24 psychotropic drugs with the GR.