ArticleThe journal of physical chemistry. B2021
A Data-Driven Dimensionality Reduction Approach to Compare and Classify Lipid Force Fields.
Article in The journal of physical chemistry. B, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
- Molecular Insights into Caffeine Stacking, Partitioning, and Organization in DPPC Lipid Bilayers from Microseconds-Long Molecular Dynamics Simulations.International journal of molecular sciences · 2026Article
- Quantifying Small-Molecule Association with Lipid Membranes: Methods, Models, and Limitations.Membranes · 2026Review
- Martini 3 as a Transferable Force Field for Lipopolysaccharide Parametrization.The journal of physical chemistry. B · 2026Article
- Classification and spatiotemporal correlation of dominant fluctuations in complex dynamical systems.PNAS nexus · 2025Article
- Layer-by-layer unsupervised clustering of statistically relevant fluctuations in noisy time-series data of complex dynamical systems.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Sampling Real-Time Atomic Dynamics in Metal Nanoparticles by Combining Experiments, Simulations, and Machine Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Data-driven discovery of innate immunomodulatorsChemical science · 2023Article
- Detecting dynamic domains and local fluctuations in complex molecular systems via timelapse neighbors shuffling.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Machine learning of atomic dynamics and statistical surface identities in gold nanoparticles.Communications chemistry · 2023Article
- Unsupervised Data-Driven Reconstruction of Molecular Motifs in Simple to Complex Dynamic Micelles.The journal of physical chemistry. B · 2023Article
- DADApy: Distance-based analysis of data-manifolds in Python.Patterns (New York, N.Y.) · 2022Article
- Classifying soft self-assembled materials via unsupervised machine learning of defects.Communications chemistry · 2022Article
- Neural Upscaling from Residue-Level Protein Structure Networks to Atomistic Structures.Biomolecules · 2021Article
- Subdiffusive-Brownian crossover in membrane proteins: a generalized Langevin equation-based approach.Biophysical journal · 2021Article
- Mechanistic Understanding from Molecular Dynamics in Pharmaceutical Research 2: Lipid Membrane in Drug Design.Pharmaceuticals (Basel, Switzerland) · 2021Review
- Living supramolecular polymerization of fluorinated cyclohexanes.Nature communications · 2021Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Molecular dynamics simulations of all-atom and coarse-grained lipid bilayer models are increasingly used to obtain useful insights for understanding the structural dynamics of these assemblies. In this context, one crucial point concerns the comparison of the performance and accuracy of classical force fields (FFs), which sometimes remains elusive. To date, the assessments performed on different classical potentials are mostly based on the comparison with experimental observables, which typically regard average properties. However, local differences of the structure and dynamics, which are poorly captured by average measurements, can make a difference, but these are nontrivial to catch. Here, we propose an agnostic way to compare different FFs at different resolutions (atomistic, united-atom, and coarse-grained), by means of a high-dimensional similarity metrics built on the framework of Smooth Overlap of Atomic Position (SOAP). We compare and classify a set of 13 FFs, modeling 1-palmitoyl-2-oleoyl-
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