MDGen

What is MDGen?

MDGen generates molecular dynamics trajectories using generative AI rather than physics-based simulation. Given a single protein structure, it produces a sequence of conformations representing how the protein might move over time—achieving speedups of 10–1000× compared to traditional MD while preserving key dynamic properties.

The model learns from molecular dynamics simulation data to capture realistic protein motions. Unlike physics-based simulators that integrate equations of motion at femtosecond timesteps, MDGen directly generates trajectory frames, making it practical to explore conformational ensembles in seconds rather than days.

How MDGen works

MDGen frames trajectory generation as a conditional generative modeling problem. The model is trained on molecular dynamics simulation data and learns to generate plausible time evolutions by conditioning on trajectory frames.

Architecture

The system uses a Scalable Interpolant Transformer (SiT) as its flow-based generative backbone. This avoids the computationally expensive residue-pair and frame-based architectures common in protein structure prediction. To handle long trajectories, MDGen incorporates the Hyena long-context architecture, enabling scaling to trajectories of 100,000+ frames.

Proteins are represented in the atom14 format (14 atoms per residue) and converted to SE(3) rigid frames (translation + rotation) plus torsion angles. This representation captures both backbone geometry and sidechain conformations.

Training data

MDGen provides checkpoints trained on different datasets:

• Tetrapeptides: Explicit and implicit solvent simulations of 4-residue peptides, used for method validation
• ATLAS: The ATLAS dataset of protein monomer simulations, preprocessed to 400 picosecond intervals, enabling generation for full proteins

Supported tasks

The generative approach enables multiple tasks through different conditioning strategies:

How to use MDGen online

ProteinIQ hosts MDGen on GPU infrastructure with pre-loaded model weights, generating trajectories directly in the browser.

Input

Settings

Trajectory parameters

Advanced options

Output

MDGen produces a trajectory viewable in the integrated 3D viewer:

The viewer supports playback controls, frame-by-frame navigation, and structure alignment.

When to use MDGen vs traditional MD

MDGen excels at rapid conformational exploration when physical accuracy is less critical than speed:

For applications requiring accurate free energy estimates, specific timescale information, or force field validation, physics-based MD remains the appropriate choice.

Limitations

MDGen is designed for research exploration and has several constraints:

• Protein size: Best results for proteins under 256 residues (ATLAS training limit); larger proteins may produce less reliable dynamics
• Physical accuracy: Generated trajectories approximate, but do not exactly reproduce, true molecular dynamics
• Timescales: The model captures conformational diversity but not absolute timescale information
• Ligands and cofactors: Currently supports protein-only trajectories; bound ligands are not handled

Interpreting results

RMSD values

Root-mean-square deviation measures how much the structure changes from the starting conformation:

Trajectory quality

Evaluate generated trajectories by checking:

• Continuity: Frames should show smooth transitions without sudden jumps
• Physical plausibility: No steric clashes, bond lengths should remain reasonable
• Diversity: Multiple conformational states should be sampled

Related tools

• OpenMM: Physics-based molecular dynamics with explicit solvent and force field control
• AlphaFlow: Flow-matching ensemble generation from protein sequences
• PDB Viewer: Structure visualization without trajectory playback
• RMSD Calculator: Quantitative structural comparison between conformations