NNP/MM: Accelerating molecular dynamics simulations with machine learning potentials and molecular mechanic

Raimondas Galvelis; Alejandro Varela-Rial; Stefan Doerr; Roberto Fino; Peter Eastman; Thomas E. Markland; John D. Chodera; Gianni De Fabritiis

arXiv · 2201.08110

NNP/MM: Accelerating molecular dynamics simulations with machine learning potentials and molecular mechanic

Raimondas Galvelis·Alejandro Varela-Rial·Stefan Doerr·Roberto Fino·Peter Eastman·Thomas E. Markland·John D. Chodera·Gianni De Fabritiis

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Abstract

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.

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BibTeX RIS

Raimondas Galvelis, Alejandro Varela-Rial, Stefan Doerr, Roberto Fino, Peter Eastman, Thomas E. Markland, John D. Chodera, Gianni De Fabritiis. 2022-01-20. NNP/MM: Accelerating molecular dynamics simulations with machine learning potentials and molecular mechanic. https://arxiv.org/abs/2201.08110

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