mlsanityData to 7 Oct 2026

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

arXiv 2201.08110 ↗First posted 20 Jan 2022q-bio.BM cs.LG physics.bio-phcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 10 of its 66 references that are in the map. First posted on arXiv 20 Jan 2022.

Builds on

10 of 66 references.

References · 10

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2021E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentialscited by 701
  3. 2021Equivariant message passing for the prediction of tensorial properties and molecular spectracited by 438
  4. 2020Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Moleculescited by 254
  5. 2019Machine learning for molecular simulationcited by 109
  6. 2021SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effectscited by 103
  7. 2018Less is more: sampling chemical space with active learningcited by 80
  8. 2020OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Featurescited by 63
  9. 2020TorchMD: A deep learning framework for molecular simulationscited by 55
  10. 2019Schr"odinger-ANI: An Eight-Element Neural Network Interaction Potential with Greatly Expanded Coverage of Druglike Chemical Spacecited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 4; 2026: 1.

8 papers.

  1. 2023OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentialscited by 44
  2. 2024FeNNol: an Efficient and Flexible Library for Building Force-field-enhanced Neural Network Potentialscited by 6
  3. 2024On the design space between molecular mechanics and machine learning force fieldscited by 3
  4. 2025Efficient and Accurate Spatial Mixing of Machine Learned Interatomic Potentials for Materials Sciencecited by 2
  5. 2025Fast, Modular, and Differentiable Framework for Machine Learning-Enhanced Molecular Simulationscited by 2
  6. 2025QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentialscited by 1
  7. 2025Predictive Free Energy Simulations Through Hierarchical Distillation of Quantum Hamiltonianscited by 0
  8. 2026AceFF: A State-of-the-Art Machine Learning Potential for Small Moleculescited by 0