R01GM140090
Project Grant
Overview
Grant Description
OpenMM: Scalable Biomolecular Modeling, Simulation, and Machine Learning - 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.
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.
Funding Goals
NOT APPLICABLE
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Stanford,
California
94305
United States
Geographic Scope
Single Zip Code
Related Opportunity
Analysis Notes
Amendment Since initial award the End Date has been extended from 03/31/25 to 05/31/29 and the total obligations have increased 638% from $426,294 to $3,145,949.
The Leland Stanford Junior University was awarded
OpenMM: Scalable Biomolecular Modeling & Simulation ML-Powered Research
Project Grant R01GM140090
worth $3,145,949
from the National Institute of General Medical Sciences in July 2021 with work to be completed primarily in Stanford California United States.
The grant
has a duration of 7 years 10 months and
was awarded through assistance program 93.859 Biomedical Research and Research Training.
The Project Grant was awarded through grant opportunity Focused Technology Research and Development (R01 Clinical Trial Not Allowed).
Status
(Ongoing)
Last Modified 6/5/26
Period of Performance
7/1/21
Start Date
5/31/29
End Date
Funding Split
$3.1M
Federal Obligation
$0.0
Non-Federal Obligation
$3.1M
Total Obligated
Activity Timeline
Subgrant Awards
Disclosed subgrants for R01GM140090
Transaction History
Modifications to R01GM140090
Additional Detail
Award ID FAIN
R01GM140090
SAI Number
R01GM140090-2384107352
Award ID URI
SAI UNAVAILABLE
Awardee Classifications
Private Institution Of Higher Education
Awarding Office
75NS00 NIH National Institute of General Medical Sciences
Funding Office
75NS00 NIH National Institute of General Medical Sciences
Awardee UEI
HJD6G4D6TJY5
Awardee CAGE
1KN27
Performance District
CA-16
Senators
Dianne Feinstein
Alejandro Padilla
Alejandro Padilla
Budget Funding
| Federal Account | Budget Subfunction | Object Class | Total | Percentage |
|---|---|---|---|---|
| National Institute of General Medical Sciences, National Institutes of Health, Health and Human Services (075-0851) | Health research and training | Grants, subsidies, and contributions (41.0) | $1,066,928 | 100% |
Modified: 6/5/26