Despite the ubiquity of stacking interactions between heterocycles and aromatic amino acids in biological systems, our ability to predict their strength, even qualitatively, is limited. On the basis of rigorous ab initio data, we developed simple predictive models of the strength of stacking interactions between heterocycles commonly found in biologically active molecules and the amino acid side chains Phe, Tyr, and Trp. These models provide reliable predictions of the stacking ability of a given heterocycle based on readily computed heterocycle descriptors, eliminating the need for quantum chemical computations of stacked dimers. We show that the values of these descriptors, and therefore the strength of stacking interactions with aromatic amino acid side chains, follow predictable trends and can be modulated by changing the number and distribution of heteroatoms within the heterocycle. This provides a simple conceptual means for understanding stacking interactions in protein binding sites and tuning their strength in the context of drug design.

Stacking interactions can be important enthalpic contributors to drug binding. Among the less well-studied stacking interactions are those occurring between an arene and the π-face of an amide group. Given the ubiquity of heterocycles in drugs, combined with the abundance of amides in the protein backbone, optimizing these noncovalent interactions can provide a potential route to enhanced drug binding. Previously, Diederich et al. (ChemMedChem 2013, 8, 397-404) studied stacked dimers of a model amide with a set of 18 heterocycles, showing that computed interaction energies correlate with the dipole moments of the heterocycles and providing guidelines for the optimization of these interactions. We considered stacked dimers of the same model amide with a larger set of 28 heterocycles common in pharmaceuticals, by using more robust ab initio methods. While the overall trends in these new data corroborate many of the results of Diederich et al., these data provide a more refined view of the nature of amide stacking interactions. We present a robust scoring function for amide stacking interaction energies based on the molecular dipole moment and strength of the electric field above the arene.