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

2022/01/20 by Raimondas Galvelis, Galvelis, Raimondas, Alejandro Varela‐Rial +13 · 5 citations
Biochemistry, Genetics and Molecular Biology · Engineering · Materials Science · #Biological Physics (physics.bio-ph) #Biomolecules (q-bio.BM) #Computational Physics (physics.comp-ph) #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Physical sciences #Fuel Cells and Related Materials #Machine Learning (cs.LG) #Machine Learning in Materials Science #Protein Structure and Dynamics

paper · pdf · doi:10.48550/arxiv.2201.08110

openalex publication_date 2022/01/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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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