Recent advances in machine learning (ML) have enabled new developments in molecular dynamics simulation. Neural network potentials (NNPs) trained on quantum mechanical (QM) data provide highly accurate descriptions of drug-like molecules. Analogous to a QM and molecular mechanical (QM/MM) approach, hybrid ML/MM simulations employ NNPs to describe a localized region of the system, such as a ligand, while the rest of the system is treated using classical MM force fields. This hybrid framework enables simulations of protein-ligand complexes with near-QM accuracy for the ligand at a substantially reduced computational cost. CHARMM-GUI Hybrid ML/MM Builder automates the preparation of system and input files required for hybrid ML/MM modeling and simulation. This new module generates all necessary files to simulate protein-ligand complexes in solution or membrane using TorchANI-AMBER and OpenMM-ML. Currently supported NNPs include MACE and ANI. In this paper, we present Hybrid ML/MM Builder and representative application systems that demonstrate its usage and capabilities.
Szczepaniak et al. (Mon,) studied this question.