ArticleJournal of chemical theory and computation2021
Ensembles Are Required to Handle Aleatoric and Parametric Uncertainty in Molecular Dynamics Simulation.
Article in Journal of chemical theory and computation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed.
- Uncertainty Quantification forChemical reviews · 2026Review
- A FAIR-Compliant Management Solution for Molecular Simulation Trajectories.Journal of chemical information and modeling · 2025Article
- Impact of Varying Velocities and Solvation Boxes on Alchemical Free-Energy Simulations.Journal of chemical information and modeling · 2025Article
- Rapid, Accurate and Reproducible Prediction of the Glass Transition Temperature Using Ensemble-Based Molecular Dynamics Simulation.Journal of chemical theory and computation · 2025Article
- Equilibrium and Nonequilibrium Ensemble Methods for Accurate, Precise and Reproducible Absolute Binding Free Energy Calculations.Journal of chemical theory and computation · 2025Article
- Evaluation of Sampling Algorithms Used for Bayesian Uncertainty Quantification of Molecular Dynamics Force Fields.Journal of chemical theory and computation · 2024Article
- Structural biology in the age of X-ray free-electron lasers and exascale computing.Current opinion in structural biology · 2024Review
- Uncertainty quantification of the lattice Boltzmann method focussing on studies of human-scale vascular blood flow.Scientific reports · 2024Article
- Ensemble-Based Approaches Ensure Reliability and Reproducibility.Journal of chemical information and modeling · 2023Article
- Comparison of Equilibrium and Nonequilibrium Approaches for Relative Binding Free Energy Predictions.Journal of chemical theory and computation · 2023Article
- Structure and dynamics of an archetypal DNA nanoarchitecture revealed via cryo-EM and molecular dynamics simulations.Nature communications · 2023Article
- An overview of the vaccine platforms to combat COVID-19 with a focus on the subunit vaccines.Progress in biophysics and molecular biology · 2023Review
- Mechanically Stable Ultrathin Layered Graphene Nanocomposites Alleviate Residual Interfacial Stresses: Implications for Nanoelectromechanical Systems.ACS applied nano materials · 2022Article
- Molecular Mechanism of Protein Arginine Deiminase 2: A Study Involving Multiple Microsecond Long Molecular Dynamics Simulations.Biochemistry · 2022Article
- The performance of ensemble-based free energy protocols in computing binding affinities to ROS1 kinase.Scientific reports · 2022Article
- Alchemical Free Energy Estimators and Molecular Dynamics Engines: Accuracy, Precision, and Reproducibility.Journal of chemical theory and computation · 2022Article
- Ensemble Simulations and Experimental Free Energy Distributions: Evaluation and Characterization of Isoxazole Amides as SMYD3 Inhibitors.Journal of chemical information and modeling · 2022Article
- Large Scale Study of Ligand-Protein Relative Binding Free Energy Calculations: Actionable Predictions from Statistically Robust Protocols.Journal of chemical theory and computation · 2022Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Classical molecular dynamics is a computer simulation technique that is in widespread use across many areas of science, from physics and chemistry to materials, biology, and medicine. The method continues to attract criticism due its oft-reported lack of reproducibility which is in part due to a failure to submit it to reliable uncertainty quantification (UQ). Here we show that the uncertainty arises from a combination of (i) the input parameters and (ii) the intrinsic stochasticity of the method controlled by the random seeds. To illustrate the situation, we make a systematic UQ analysis of a widely used molecular dynamics code (NAMD), applied to estimate binding free energy of a ligand-bound to a protein. In particular, we replace the usually fixed input parameters with random variables, systematically distributed about their mean values, and study the resulting distribution of the simulation output. We also perform a sensitivity analysis, which reveals that, out of a total of 175 parameters, just six dominate the variance in the code output. Furthermore, we show that binding energy calculations dampen the input uncertainty, in the sense that the variation around the mean output free energy is less than the variation around the mean of the assumed input distributions, if the output is ensemble-averaged over the random seeds. Without such ensemble averaging, the predicted free energy is five times more uncertain. The distribution of the predicted properties is thus strongly dependent upon the random seed. Owing to this substantial uncertainty, robust statistical measures of uncertainty in molecular dynamics simulation require the use of ensembles in all contexts.
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