Converting SMILES to Stacking Interaction Energies.

Predicting the strength of stacking interactions involving heterocycles is vital for several fields, including structure-based drug design. While quantum chemical computations can provide accurate stacking interaction energies, these come at a steep computational cost. To address this challenge, we recently developed quantitative predictive models of stacking interactions between druglike heterocycles and the aromatic amino acids Phe, Tyr, and Trp (DOI: 10.1021/jacs.9b00936 ). These models depend on heterocycle descriptors derived from electrostatic potentials (ESPs) computed using density functional theory and provide accurate stacking interactions without the need for expensive computations on stacked dimers. Herein, we show that these ESP-based descriptors can be reliably evaluated directly from the atom connectivity of the heterocycle, providing a means of predicting both the descriptors and the potential for a given heterocycle to engage in stacking interactions without resorting to any quantum chemical computations. This enables the rapid conversion of simple molecular representations (e.g., SMILES) directly into accurate stacking interaction energies using a freely available online tool, thereby providing a way to rank the stacking abilities of large sets of heterocycles.