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MD simulations

Created Apr 10, 2026Modified Apr 17, 2026

Molecular dynamics (MD) simulations are a computational tool for Newtonian modeling of biomolecules over the course of time.

Uses

Free energy perturbation

Types of ensembles

NPT ensemble: Gibbs free energy is at a minimum
NVT ensemble: Helmholtz free energy is at a minimum
NVE ensemble: Entropy is maximal

Graph View

Backlinks

Abs modeled using MD are better for featurization than those modeled using homology modeling
Alpha carbon-only representations are sufficient for ML force fields
AlphaFlow and ESMFlow
Antibody developability predictions from computational models are sensitive to the structure prediction method
Antibody-antigen binding modes are not necessarily defined by induced fit
Arginine residues in CDRH3 increases promiscuity
Bias-exchange metadynamics
Boltzmann distribution
Collective variables
Conformational entropy in antibodies decreases during affinity maturation
Enhanced sampling
Features for antibody property prediction derived from MD simulations outperform those from language models and static structures
Free energy perturbation
Helmholtz free energy
High-stability substructures fold first
Hydrogen mass repartitioning
Inverse folding from MD simulations
Likelihood of predicted contacts by MSA transformer weakly correlate with dwell time in molecular dynamics simulations
Linear MD encoders outperform nonlinear encoders when extrapolating to unseen regions of protein conformational space
MD force fields can be screened for agreement with experimental data
MD potentials from ML are more effective when protein-specific
MD simulations can distinguish enzyme design candidates for catalytically competent substates
MD trajectories reproduce experimental dG values to within 1 kcal per mol
Machine learning force fields for molecular dynamics require prior potentials
Markov State Models
Metadynamics
Molecular dynamics simulation of an entire cell
Most proteins have a single folding route
NPT ensemble
NVE ensemble
NVT ensemble
Protein dynamics can be used as filtering criteria for the design of highly selective small molecules
Psi torsion angles are effective metadynamic CVs for CDRH3 and CDRL3
QM-MM and unbiased MD are insufficient to correctly determine CDRH3 conformation
Replica-exchange molecular dynamics
Structural tokens improve zero-shot variant effect prediction in ESM3, but only when structures are not computationally derived
Structure ensemble prediction methods are unable to accurately model subtle conformational differences captured by X-ray crystallography
The latent space of VAEs can encode the conformational landscape of dynamic proteins
Thermostable proteins have more small aliphatic amino acids that facilitate more flexible contacts
Time-lagged independent component analysis
Variable regions can adopt multiple interchain orientations, and these are difficult to predict
X-ray density can capture alternate conformations of proteins and their ligands
pLDDT and PAE inversely correlated with protein dynamics in dynamic naturally occurring proteins, but not de novo proteins

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