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Attribute
Research Tooling & Frameworks

OpenMM

Stanford University (Pande Lab)
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CategoryResearch Tooling & Frameworks
CompanyStanford University (Pande Lab)
PriceFree Open Source
StatusStatus not confirmed
AvailabilityAvailable via Conda (conda-forge)
Last checked20/09/2026
FeaturesExtremely 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
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