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Research Tooling & MLOps · Molecular Dynamics Toolkits

OpenMM

By Stanford University (Pande Lab) →

High-performance GPU toolkit for molecular dynamics simulation and machine learning integration

OpenMM is a high-performance extensible toolkit for molecular simulation, engineered specifically to leverage modern GPUs and integrate seamlessly with deep learning frameworks like PyTorch.

Extremely high simulation speed on NVIDIA CUDA, OpenCL, and AMD ROCm GPUsPython API enabling custom force fields, non-equilibrium dynamics, and active learningOpenMM-Torch plugin for incorporating neural network potentials (ANI, MACE) into simulationsSupport for AMBER, CHARMM, GROMOS, and OpenFF force fields
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Overview

OpenMM is a high-performance extensible toolkit for molecular simulation, engineered specifically to leverage modern GPUs and integrate seamlessly with deep learning frameworks like PyTorch.

Information checked against an official source; not a hands-on test. Source · Last reviewed: 20/09/2026, 11:09:40

Key Features

  • Extremely high simulation speed on NVIDIA CUDA, OpenCL, and AMD ROCm GPUs
  • Python API enabling custom force fields, non-equilibrium dynamics, and active learning
  • OpenMM-Torch plugin for incorporating neural network potentials (ANI, MACE) into simulations
  • Support for AMBER, CHARMM, GROMOS, and OpenFF force fields

Academic Context & Research Evidence

Biological & Workflow Fit

Biological Application
Protein folding dynamics, ligand binding kinetics, and free energy calculations
Research Workflow
PDB structure -> parameterization with force field -> GPU-accelerated MD trajectory
Compute & Hardware
NVIDIA or AMD GPU for maximum simulation throughput
Licensing & Academic Use
MIT & LGPL
Documented Evidence
View validation publication / source ↗

Cite this Tool

Use this citation format when referencing OpenMM in scientific publications and benchmark papers.

@software{openmm_2026,
  title = {{OpenMM}},
  author = {{Stanford University (Pande Lab)}},
  year = {2026},
  url = {https://openmm.org},
  note = {Indexed on aibioatlas - AI for Biology and Drug Discovery}
}

Peer-Reviewed Literature & Preprints

Live scientific citations streamed from Europe PMC and PubMed for OpenMM.

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Technical / Product Information

Missing values mean the catalog has no recorded information. They do not mean a feature is absent.

Entry typeSoftware Toolkit
Access modeOpen Source
AI rolePhysics & Neural Force Field Simulation
Input dataNot recorded
Output dataNot recorded
Licence conditionsMIT & LGPL
Commercial eligibilityPermissive open source release
Compute requirementsNVIDIA or AMD GPU for maximum simulation throughput
ValidationNot recorded
TypeMolecular dynamics simulation library
Intended useNot recorded
CompatibilityNot recorded
ManufacturerStanford University & OpenMM Contributors
Biological applicationProtein folding dynamics, ligand binding kinetics, and free energy calculations
Research workflowPDB structure -> parameterization with force field -> GPU-accelerated MD trajectory
Evidence levelPeer-reviewed research with widespread adoption in biophysics and drug design
Integration evidencehttps://openmm.org
Laboratory handoffIdentifies transient binding pockets for experimental hit discovery
AvailabilityAvailable via Conda (conda-forge)
Price / accessFree Open Source

Research fit & compatibility

No software–hardware integration has been verified for this entry yet. Explore documented research workflows.

Gallery & resources

No licensed product photograph recorded for OpenMM yet. Visit the official resource below for manufacturer imagery.

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FAQ

Where is this product available?

Available via Conda (conda-forge)

How is pricing handled?

Prices reflect the source at its last check. Confirm current pricing and regional availability on the official site.

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Peer Reviews & Community Ratings

Feedback from researchers and computational biologists evaluating OpenMM.

5.0
★★★★★Based on 0 researcher evaluations
Biological Accuracy
4.8/5
Ease of Installation
4.3/5
Documentation & Code
4.6/5