Back to EOSS Proposals List

Back to All Open Science Grantees

OpenMM: Key Infrastructure for Biomolecular Modeling and Simulation

Project OpenMM: A high performance toolkit for molecular simulation
Funding Cycle 2

Proposal Summary

This team will support the continued development of OpenMM to better serve its broad biomolecular modeling community, as well as support its extension to integrate machine learning that will enable genomic-scale biomolecular modeling, simulation, and prediction.


OpenMM: A high performance toolkit for molecular simulation

OpenMM is the most widely-used open source GPU-accelerated framework for biomolecular modeling and simulation. 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, the team work will focus on the 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, they 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 100M+ 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 Personnel

Thomas Markland
John Chodera
Peter Eastman
Gianni De Fabritiis
Raimondas Galvelis
Karmen Condic-Jurkic
Ana Silveira