Early prediction of ADME properties such as the cytochrome P450 (CYP) mediated drug-drug interactions is an important challenge in the drug discovery area. In this study, we propose to couple an original data mining approach based on Rough Set Theory (RST) to a structural description of molecules. The latter was achieved by using two types of structural keys: (1) the MACCS keys and (2) a set of five in-house fingerprints based on properties of the electron density distributions of chemical groups. The compounds considered are involved in the inhibition of CYP1A2 and CYP2D6. RST allowed the extraction of rules further used as classifiers to predict the inhibitory profile of an independent set of molecules. The results reached prediction accuracies of 90.6 and 88.2 % for CYP1A2 and CYP2D6, respectively. In addition, these classifiers were analyzed to determine which structural fragments were most used for building the rules, revealing relationships between the occurrence of particular molecular fragments and CYP inhibition. The results assessed RST as a suitable tool to build strongly predictive models and infer structure-activity rules associated with potency.