ArticleThe Journal of chemical physics2023
Neural potentials of proteins extrapolate beyond training data.
Article in The Journal of chemical physics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Learning data-efficient coarse-grained molecular dynamics from forces and noise.Nature communications · 2026Article
- Uncertainty in the era of machine learning for atomistic modeling.Digital discovery · 2025Review
- Development of Coarse-Grained Lipid Force Fields Based on a Graph Neural Network.Journal of chemical theory and computation · 2025Article
- Navigating protein landscapes with a machine-learned transferable coarse-grained model.Nature chemistry · 2025Article
- On the emergence of machine-learning methods in bottom-up coarse-graining.Current opinion in structural biology · 2025Review
- The Potential of Neural Network Potentials.ACS physical chemistry Au · 2024Review
- Transferable Implicit Solvation via Contrastive Learning of Graph Neural Networks.ACS central science · 2023Article
- Transferable Coarse Graining via Contrastive Learning of Graph Neural Networks.bioRxiv : the preprint server for biology · 2023Article
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Authors and funding
3 authors.
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Abstract
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.
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Registered trials
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