This study describes the generation of a three-dimensional quantitative structure activity relationship (3D-QSAR) model for 29 structurally diverse, competitive CYP2C9 inhibitors defined experimentally from an initial data set of 73 compounds. In parallel, a homology model for CYP2C9 using the rabbit CYP2C5 coordinates was built. For molecules with a known interaction mode with CYP2C9, this homology model, in combination with the docking program GOLD, was used to select conformers to use in the 3D-QSAR analysis. The remaining molecules were docked, and the GRID interaction energies for all conformers proposed by GOLD were calculated. This was followed by a principal component analysis (PCA) of the GRID energies for all conformers of all compounds. Based on the similarity in the PCA plot to the inhibitors with a known interaction mode, the conformer to be used in the 3D-QSAR analysis was selected. The compounds were randomly divided into two groups, the training data set (n = 21) to build the model and the external validation set (n = 8). The PLS (partial least-squares) analysis of the interaction energies against the K(i) values generated a model with r(2) = 0.947 and a cross-validation of q(2) = 0.730. The model was able to predict the entire external data set within 0.5 log units of the experimental K(i) values. The amino acids in the active site showed complementary features to the grid interaction energies in the 3D-QSAR model and were also in agreement with mutagenesis studies.