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OpenMM Scientific Architecture

OpenMM Scientific Architecture · Stanford University (Pande Lab)

Research Tooling & Frameworks · Molecular Dynamics Toolkits

OpenMM

开发机构 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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概览介绍

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.

信息已比对权威文献或官方白皮书；非实验性跑分。权威出处 · 最后核对: 20/09/2026, 11:09:40 UTC

核心功能特性

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 queried from Europe PMC and PubMed for OpenMM.

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技术参数与产品规格

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

OpenMM Scientific Architecture

OpenMM Scientific Architecture · Credit: Stanford University (Pande Lab) · Open original ↗

Research and editorial use Source & attribution ↗ License ↗

Resource: OpenMM Official Site ↗ · Stanford University

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最新研究动态

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常见问题解答

该工具在哪些平台或地区可用？

Available via Conda (conda-forge)

商业授权与费用如何计算？

费用根据官方最近一次公布的说明整理。请在官方网站确认最新报价与学术折扣。

Peer Reviews & Community Ratings

Feedback from researchers and computational biologists evaluating OpenMM.

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