DoMCoSAR is a novel approach for statistically determining the docking mode that is consistent with a structure-activity relationship. The approach establishes the binding mode for the compounds in a chemical series with the assumption that all molecules exhibit the same binding mode. It involves three stages. In the first stage all molecules that belong to a given chemical series are docked to the active site of the protein target. The only bias used in the docking at this stage involves the location of the protein binding site. Coordinates of the common substructure (CS) that results from the unbiased docking are then clustered to establish the major substructure docking modes. In the second stage all molecules are docked to the major docking modes (MDMs) with constraints based on the common substructure. The third stage generates, for the major docking modes, interaction-based descriptors that include electrostatic, VDW, strain, and solvation contributions. The problem of docking mode evaluation is now reduced to the question of which descriptor set is more predictive. To establish a quantitative comparison of the descriptor sets associated with the major docking modes, we use 50 instances of random 4-fold cross-validation. For each 4-fold cross-validation the predictive squared correlation coefficient (R(2)) is computed. t-Tests are applied to establish significance of the differences in mean R(2) for one docking mode versus another. We test the methodology on two test cases: HIV-1 protease inhibitors (Holloway et al. J. Med. Chem. 1995, 38, 305-317) and vascular endothelial growth factor (VEGF) receptor tyrosine kinase oxoindoles (Sun et al. J. Med. Chem. 1998, 41, 2588-2603). For both test cases there is statistically significant preference for the binding mode consistent with the X-ray structure. The appeal of this methodology is that researchers gain the objectivity of statistical justification for the selected docking mode. The methodology is relatively insensitive to subtle variations of the protein structure that include, but are not limited to, side chain and small backbone rearrangement during binding. In addition, predictive models that result from the approach can be used to further optimize chemical series.