NIH awards initial $68M for AI-driven Structure-enabled Antiviral Platform (ASAP) for open science discovery of oral antivirals for pandemic preparedness

We are excited to announce that the NIH has awarded an initial $68M of funding for the first three years of the AI-driven Structure-enabled Antiviral Platform (ASAP) as one of the NIAID-funded U19 Antiviral Drug Discovery (AViDD) Centers. Led by PIs John Chodera (MSKCC), Ben Perry (DNDi), and Alpha Lee (PostEra), ASAP builds on our earlier work with the COVID Moonshot, which delivered a SARS-CoV-2 oral antiviral preclinical candidate in 18 months, and will develop an open global oral antiviral pipeline with the goal of delivering medicines for globally equitable and affordable access in partnership with the Drugs for Neglected Diseases Initiative (DNDi).

[ASAP concept] [DNDi press release] [ASAP website]

OpenMM secures federal funding though an NIH NIGMS R01 grant

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.

Securing sustainable funding to enable OpenMM to continue to power the next decade in biomolecular modeling and simulation

OpenMM is the most widely-used open source GPU-accelerated framework for biomolecular modeling and simulation. It has been cited more than 1300 times, downloaded over 280,000 times from conda-forge alone, and has run on more than one million distinct computers. 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 $16B 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 now 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, guided by the OpenMM Consortium. To fully exploit the revolution in QM-level accuracy with quantum machine-learning (QML) potentials, we also plan to add plug-in support for QML 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.

Recently, we applied for federal funding to realize this vision via a new NIH Focused Technology Research & Development R01 proposal, with strong support from the biomolecular simulation community. You can read the scientific components of the proposal we submitted here: [PDF]

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