OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

Eastman P, Galvelis R, Peláez RP, Abreu CRA, Farr SE, Gallicchio E, Gorenko A, Henry MH, Hu F, Huang J, Krämer A, Michel J, Mitchell J, Pande VS, Rodrigues JPGLM, Rodriguez-Guerra J, Simmonett AC, Swails J, Turner P, Wang Y, Zhang I, Chodera JD, De Fabritiis G, Markland TE
Journal of Physical Chemistry B [DOI] [website] [code]

We present OpenMM 8, which includes GPU-accelerated support for simulating hybrid ML/MM systems that use machine learning (ML) potentials to achieve high accuracy with minimal loss in speed.

NNP/MM: Fast molecular dynamics simulations with machine learning potentials and molecular mechanics

Galvelis R, Varela-Rial A, Doerr S, Fino R, Eastman P, Markland TE,  Chodera JD, and de Fabritiis G
Journal of Chemical Information and Modeling 63:5701, 2023 [DOI] [arXiv]

We demonstrate that a new generation of quantum machine learning (QML) potentials based on neural networks---which can achieve quantum chemical accuracy at a fraction of the cost---can be implemented efficiently in the OpenMM molecular dynamics simulation engine as part of hybrid machine learning / molecular mechanics (ML/MM) potentials that promise to deliver superior accuracy for modeling protein-ligand interactions.

SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials

Eastman P, Behara PK, Dotson DL, Galvelis R, Herr JE, Horton JT, Mao Y, Chodera JD, Pritchard BP, Wang Y, De Fabritiis G, and Markland TE
Scientific Data 10:11, 2023 [DOI]

To remedy the lack of large, open quantum chemical datasets for training accurate general machine learning potentials and molecular mechanics force fields for druglike small molecules and biomolecules, we produce the open SPICE dataset, and show how it can be used to build extremely accurate machine learning potentials.

Escaping atom types in force fields using direct chemical perception

David L. Mobley, Caitlin C. Bannan, Andrea Rizzi, Christopher I. Bayly, John D. Chodera, Victoria T Lim, Nathan M. Lim, Kyle A. Beauchamp, Michael R. Shirts, Michael K. Gilson, Peter K. Eastman.
Journal of Chemical Theory and Computation 14:6076, 2018 [DOI] [bioRxiv]

We describe the philosophy behind a modern approach to molecular mechanics forcefield parameterization, and present initial results for the first SMIRNOFF-encoded forcefield: SMIRNOFF99Frosst.

OpenMM 7: Rapid Development of High Performance Algorithms for Molecular Dynamics

Peter Eastman, Jason Swails, John D. Chodera, Robert T. McGibbon, Yutong Zhao, Kyle A. Beauchamp, Lee-Ping Wang, Andrew C. Simmonett, Matthew P. Harrigan, Chaya D. Stern, Rafal P. Wiewiora, Bernard R. Brooks, Vijay S. Pande. PLoS Computational Biology 13:e1005659, 2017. [DOI] [bioRxiv] [website] [GitHub]

We describe the latest version of OpenMM, a GPU-accelerated framework for high performance molecular simulation applications.

OpenMM 4: A reusable, extensible, hardware independent library for high performance molecular simulation

Peter Eastman, Mark S. Friedrichs, John D. Chodera, Randy J. Radmer, Chris M. Bruns, Joy P. Ku, Kyle A. Beauchamp, T. J. Lane, Lee-Ping Wang, Diwakar Shukla, Tony Tye, Mike Houston, Timo Stich, Christoph Klein, Michael R. Shirts, and Vijay S. Pande.
J. Chem. Theor. Comput. 9:461, 2013. [DOI] [PDF]

We describe the latest version of an open-source, GPU-accelerated library and toolkit for molecular simulation.