A new QSAR method based on approximate similarity measurements is described in this paper. Approximate similarity is calculated using both the classical similarity based on the graph isomorphism and a distance computation between nonisomorphic subgraphs. The latter is carried out through a parametric function where different topological invariants can be considered. After optimizing the contribution of nonisomorphic distance to the new graph similarity, predictive models built with approximate similarity matrixes show higher predictive ability than those using traditional similarity matrixes. The new method has been applied to the prediction of steroids binding to the corticosteroid globulin receptor. The proposed model allows us to obtain valuable external predictions (r = 0.82 and SEP = 0.30) after training the model by cross-validation (Q2 = 0.84 and SECV = 0.47). Slope and bias parameters are also given.