# OpenMM: Scalable biomolecular modeling, simulation, and machine learning

> **NIH NIH R01** · STANFORD UNIVERSITY · 2022 · $123,750

## Abstract

PROJECT SUMMARY / ABSTRACT
OpenMM [http://openmm.org] is the most widely-used open source GPU-accelerated framework for
biomolecular modeling and simulation (>1300 citations, >270,000 downloads, >1M deployed
instances). Its Python API makes it widely popular as both an application (for modelers) and a library
(for developers), while its C/C++/Fortran bindings enable major legacy simulation packages to use
OpenMM to provide high performance on modern hardware. OpenMM has been used for probing
biological questions that leverage the $14B global investment in structural data from the PDB at
multiple scales, from detailed studies of single disease proteins to superfamily-wide modeling studies
and large-scale drug development efforts in industry and academia.
Originally developed with NIH funding by the Pande lab at Stanford, we aim to fully transition toward a
community governance and sustainable development model and extend its capabilities to ensure
OpenMM can power the next decade of biomolecular research. To fully exploit the revolution in QM-
level accuracy with machine-learning (ML) potentials, we will add plug-in support for ML models
augmented by GPU-accelerated kernels, enabling transformative science with QM-level accuracy. To
enable high-productivity development of new ML models with training dataset sizes approaching 100
million molecules, we will develop a Python framework to enable OpenMM to be easily used within
modern ML frameworks such as TensorFlow and PyTorch. Together with continued optimizations to
exploit inexpensive GPUs, these advances will power a transformation within biomolecular modeling
and simulation, much as deep learning has transformed computer vision.

## Key facts

- **NIH application ID:** 10587054
- **Project number:** 3R01GM140090-02S1
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Thomas Edward Markland
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $123,750
- **Award type:** 3
- **Project period:** 2021-07-01 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10587054

## Citation

> US National Institutes of Health, RePORTER application 10587054, OpenMM: Scalable biomolecular modeling, simulation, and machine learning (3R01GM140090-02S1). Retrieved via AI Analytics 2026-06-08 from https://api.ai-analytics.org/grant/nih/10587054. Licensed CC0.

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