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We report on a model for the interaction of chimeric melanocortin G-protein coupled receptors with peptide ligands using the rough set approach. Rough sets generate If-Then rule models using Boolean reasoning. Two separate datasets have been analyzed, for which the binding affinities have previously been measured experimentally. The receptors and ligands are described by vectors of strings. Different partitions of each dataset were evaluated in order to find an optimal partition into rough set decision classes. To obtain a measurement of the accuracy of the rough set classifier generated from each dataset, a 10-fold cross validation (CV) was performed. The Area Under Curve (AUC) was calculated for each iteration during CV. This resulted in an AUC mean of 0.94 (SD 0.12) and 0.93 (SD 0.16) for the first and second dataset respectively. The CV results show that the rough set models exhibit a high classification quality. The decision rules generated from the rough set model inductions are easy to interpret. We apply this information to develop models of the interaction between ligands and receptors.