Neural potentials of proteins extrapolate beyond training data

Geemi P. Wellawatte, Glen M. Hocky, Andrew D. White

Research output: Contribution to journalArticlepeer-review


We evaluate neural network (NN) coarse-grained (CG) force fields compared to traditional CG molecular mechanics force fields. We conclude that NN force fields are able to extrapolate and sample from unseen regions of the free energy surface when trained with limited data. Our results come from 88 NN force fields trained on different combinations of clustered free energy surfaces from four protein mapped trajectories. We used a statistical measure named total variation similarity to assess the agreement between reference free energy surfaces from mapped atomistic simulations and CG simulations from trained NN force fields. Our conclusions support the hypothesis that NN CG force fields trained with samples from one region of the proteins’ free energy surface can, indeed, extrapolate to unseen regions. Additionally, the force matching error was found to only be weakly correlated with a force field’s ability to reconstruct the correct free energy surface.

Original languageEnglish (US)
Article number085103
JournalJournal of Chemical Physics
Issue number8
StatePublished - Aug 28 2023

ASJC Scopus subject areas

  • General Physics and Astronomy
  • Physical and Theoretical Chemistry


Dive into the research topics of 'Neural potentials of proteins extrapolate beyond training data'. Together they form a unique fingerprint.

Cite this