ABSTRACT
We have classified a set of 250 benzimidazoles using a criterion of structural similarity. This criterion has led us to several clusters, which keep a close relationship between the molecules belonging to each one of them and their pharmacological activity. To study the structural similarity we have built a mathematical space where chemical structures are pictured as vectors. A set of well-chosen descriptors was used as variables. These descriptors arise from graph theoretical studies and quantum mechanical calculations. Principal components analysis was employed to find the suitable dimension for the space. Finally, cluster analysis was performed to classify the set of molecules by similarity. A Euclidean metric was used as a similarity coefficient.