OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials.


Journal

ArXiv
ISSN: 2331-8422
Titre abrégé: ArXiv
Pays: United States
ID NLM: 101759493

Informations de publication

Date de publication:
29 Nov 2023
Historique:
pubmed: 21 11 2023
medline: 21 11 2023
entrez: 21 11 2023
Statut: epublish

Résumé

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost.

Identifiants

pubmed: 37986730
pii: 2310.03121
pmc: PMC10659447
pii:

Types de publication

Preprint

Langues

eng

Auteurs

Classifications MeSH