OpenMM secures federal funding though an NIH NIGMS R01 grant

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Recently, OpenMM applied for NIH funding to seek a sustainable federal source of support to continue to serve and adapt to the changing needs of the molecular simulation community by providing a fast, flexible, and extensible platform for advanced biomolecular simulations.

We’re thrilled to report that the NIH has awarded us funding through Mar 2025 via NIH grant R01GM140090. Funding will continue to support lead OpenMM developer Peter Eastman, as well as new developers based in the computational biophysics lab headed by Gianni de Fabritiis. Together, this will enable us to not only continue to support, optimize, and maintain OpenMM, but to also extend it to take advantage of the unfolding revolution in quantum machine learning potentials that continue to transform our field.

With a newly redesigned website, a newly-established OpenMM Consortium helping steer scientific directions, recruitment of science communicator Joshua Mitchell to lead a major effort to refine our documentation and materials, added support for GPU-accelerated pytorch and tensorflow based potentials, and migration to the conda-forge ecosystem (with 158K downloads from conda-forge this year already), we’re off to a great start!

To read more about our plans to continue to extend OpenMM to tightly integrate OpenMM with modern ML frameworks such as TensorFlow, PyTorch, and JAX; allow machine learning potentials or collective variables defined in these machine learning frameworks to be easily used within OpenMM; and and Python libraries to make it easy to build next-generation hybrid quantum machine learning / molecular mechanics (QML/MM) models within these frameworks, check out our NIH research proposal here.

Thanks to all of you who submitted letters of support! Your support means the world to us.

Help us secure federal funding for OpenMM!

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Recently, OpenMM applied for NIH funding, and while we just missed being funded this round, we’re optimistic about being funded in our resubmission. Besides powering a large fraction of the biomolecular simulation community (OpenMM Has been downloaded over 380,000 times from conda alone), we’re super excited to be working be able to tightly integrate OpenMM with modern ML frameworks such as TensorFlow, PyTorch, and JAX. We are building OpenMM plugins to allow machine learning potentials or collective variables defined in these machine learning frameworks to be easily used within OpenMM, and Python libraries to make it easy to build next-generation hybrid quantum machine learning / molecular mechanics (QML/MM) models within these frameworks. Our earliest results already show that hybrid QML/MM simulations where the ligand is treated with QML can drastically reduce the error in alchemical protein-ligand binding free energy calculations from 1 kcal/mol to 0.5 kcal/mol! We’ll also build a common model repository that will allow popular QML models to easily be used in your simulations. You can read more about our plans in our research proposal, posted here.

EDIT: The submitted research proposal is available here. Thanks to all of you who submitted letters of support!

We need your help!

If you either currently use OpenMM in your research or software, or would like to in the future, please consider writing us a Letter of Support for our NIH resubmission! All you have to do is draft a letter on institutional letterhead, addressed to

Tom Markland
Associate Professor, Department of Chemistry
Stanford University

and cover any or all of the following bullet points:

  • How you currently use OpenMM in your research or software, or how you plan to use it

  • How additional support for quantum machine learning potentials, machine learning collective variables defined in PyTorch/TensorFlow/JAX, or complex integrators defined in these packages will be useful to you

  • Why you think machine learning potentials or collective variables are going to be useful for biomolecular simulation

  • How you would be able to make use of our specific proposed work in the grant proposal, such as support for advanced potential functions, continued hardware optimizations, machine learning framework plugin support, hybrid QML/MM potentials, accelerated physical MM force Op library for machine learning frameworks, or the generation of large quantum chemical datasets for biomolecules on Folding@home that will be deposited real-time into the MolSSI QCArchive quantum chemistry archive

Finally, send a PDF copy of the Letter of Support to openmm@choderalab.org by Friday 23 October.

We also welcome your feedback on how OpenMM can continue to serve the needs of the biomolecular simulation community over the next decade and beyond!

Thanks so much!

John Chodera
Tom Markland
Gianni de Fabritiis