NNP/MM: Accelerating molecular dynamics simulations with machine learning potentials and molecular mechanic
This work addresses efficiency bottlenecks for researchers in computational chemistry and drug discovery, though it is incremental as it builds on existing hybrid methods.
The paper tackled the high computational cost of machine learning potentials in biomolecular simulations by introducing an optimized hybrid method (NNP/MM) that combines neural network potentials and molecular mechanics, resulting in a 5x speed increase and enabling microsecond-scale simulations for protein-ligand complexes.
Machine learning potentials have emerged as a means to enhance the accuracy of biomolecular simulations. However, their application is constrained by the significant computational cost arising from the vast number of parameters compared to traditional molecular mechanics. To tackle this issue, we introduce an optimized implementation of the hybrid method (NNP/MM), which combines neural network potentials (NNP) and molecular mechanics (MM). This approach models a portion of the system, such as a small molecule, using NNP while employing MM for the remaining system to boost efficiency. By conducting molecular dynamics (MD) simulations on various protein-ligand complexes and metadynamics (MTD) simulations on a ligand, we showcase the capabilities of our implementation of NNP/MM. It has enabled us to increase the simulation speed by 5 times and achieve a combined sampling of one microsecond for each complex, marking the longest simulations ever reported for this class of simulation.