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

> **NIH NIH R01** · STANFORD UNIVERSITY · 2023 · $471,265

## 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:** 10589161
- **Project number:** 5R01GM140090-03
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Thomas Edward Markland
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $471,265
- **Award type:** 5
- **Project period:** 2021-07-01 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10589161

## Citation

> US National Institutes of Health, RePORTER application 10589161, OpenMM: Scalable biomolecular modeling, simulation, and machine learning (5R01GM140090-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10589161. Licensed CC0.

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