Compounds from a wide variety of structural classes inhibit HIV-1 integrase. However, a single unified understanding of the relationship between the structures and activities of these compounds still eludes researchers. We report herein the development of QSAR models for integrase inhibition. The genetic function approximation (GFA) was utilized to select descriptors for the development of the QSAR models. The best QSAR model derived for the complete set of 11 structural classes had a correlation coefficient (r(2)) of only 0.54 and a cross-validated correlation coefficient (q(2)) of only 0.42. This indicated that the compounds studied may differ in the exact relationship between structure and inhibition, perhaps through interactions with different subsets of amino acids in the binding pocket, or through the presence of non-overlapping binding pockets. Descriptor-based cluster analysis indicated that the 11 structural classes of integrase inhibitors studied belonged to two clusters, one consisting of five structural classes, and the other six. QSAR models for these two clusters had r(2) values of 0.79 and 0.82 and q(2) values of 0.71 and 0.74, a significant improvement over models obtained for the complete set of compounds. The two models were applied to predict the activities of compounds from the same structural classes as those used to build the models, giving r(2) values of 0.65 and 0.78. The models were also used to predict the activities of compounds shown in crystallographic or docking studies to interact near the active site metal ion. The model describing the larger cluster of structural classes was better able to reproduce the biological activities of these five structures with an average percent residual error of 7.9 compared with the 19.3% residual error for predictions from the other model. This indicated that the six structural classes comprising the larger cluster may bind near the metal ion in a fashion similar to that observed in one publicly available co-crystal structure of an inhibitor bound to HIV-1 integrase. Flexible alignment of inhibitors in the two clusters found different pharmacophores that are consistent with previously published pharmacophores developed on the basis of individual structural classes that have produced novel inhibitory compounds. Thus we expect that these two QSAR models can be used in the search for novel HIV-1 integrase inhibitors as well as to provide insight into the binding modes of such diverse chemical compounds.