English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Machine learning potentials have emerged as a means to enhance the accuracyof biomolecular simulations. However, their application is constrained by thesignificant computational cost arising from the vast number of parameterscompared to traditional molecular mechanics. To tackle this issue, we introducean optimized implementation of the hybrid method (NNP/MM), which combinesneural network potentials (NNP) and molecular mechanics (MM). This approachmodels a portion of the system, such as a small molecule, using NNP whileemploying MM for the remaining system to boost efficiency. By conductingmolecular dynamics (MD) simulations on various protein-ligand complexes andmetadynamics (MTD) simulations on a ligand, we showcase the capabilities of ourimplementation of NNP/MM. It has enabled us to increase the simulation speed by5 times and achieve a combined sampling of one microsecond for each complex,marking the longest simulations ever reported for this class of simulation.

NNP/MM: Accelerating molecular dynamics simulations with machine learning potentials and molecular mechanic