AromTool: predicting aromatic stacking energy using an atomic neural network model.

Aromatic stacking exists widely and plays important roles in protein-ligand interactions. Computational tools to automatically analyze the geometry and accurately calculate the energy of stacking interactions are desired for structure-based drug design. Herein, we employed a Behler-Parrinello neural network (BPNN) to build predictive models for aromatic stacking interactions and further integrated it into an open-source Python package named AromTool for benzene-containing aromatic stacking analysis. Based on extensive testing, AromTool presents desirable precision in comparison to DFT calculations and excellent efficiency for high-throughput aromatic stacking analysis of protein-ligand complexes.