Our AI-accelerated molecular dynamics platform achieves 10-100x speedup over conventional MD simulations. Access microsecond-to-millisecond dynamics that reveal conformational changes, binding mechanisms, and allosteric networks invisible to static structures.
Conventional molecular dynamics simulations are limited by computational cost. Even the most powerful supercomputers can only simulate microseconds of protein dynamics per day, while biologically relevant conformational changes often occur on milliseconds or longer timescales. Our neural network-enhanced sampling overcomes these barriers.
Neural network potentials trained on quantum mechanical data accelerate MD by 10-100x while maintaining ab initio accuracy. Access timescales prohibitive to conventional methods.
Sample millisecond conformational dynamics, including protein folding, large-scale domain motions, and ligand unbinding pathways that define biological function.
Extract transition rates between conformational states from enhanced sampling trajectories. Predict binding kinetics (kon, koff) and residence times without experimental measurement.
Visualize transition pathways, identify intermediate states, and map allosteric communication networks. Understand not just what happens, but how and why.
We integrate neural network-enhanced sampling methods with rigorous validation for accurate dynamics prediction.
Deep neural networks trained on DFT data provide quantum-mechanical accuracy at molecular dynamics speeds. Models capture electron correlation effects while enabling long simulation timescales.
Metadynamics, umbrella sampling, and adaptive bias methods accelerated by neural network collective variables. Our CV design identifies the slowest dynamical modes for efficient sampling.
All simulations are validated against experimental data when available. NMR relaxation, cryo-EM ensembles, and FRET measurements confirm prediction accuracy.
Our AI-accelerated MD platform supports a wide range of molecular dynamics studies.
Characterize protein motions across all timescales. Identify conformational states, predict population distributions, and map energy landscapes for rational engineering.
Observe complete unbinding pathways and predict residence times. Understand mechanism of action differences between compounds with similar affinities.
Observe complete folding trajectories from unfolded states. Characterize folding intermediates and transition states relevant to misfolding diseases.
Map signal propagation pathways from distant sites to functional regions. Identify key residues mediating allosteric regulation for drug targeting.
Observe enzyme conformational changes during catalysis. Identify proton transfer pathways, substrate positioning, and transition state stabilization.
Simulate GPCR conformational changes, ion channel gating, and transporter cycles in native membrane environments. Capture lipid-mediated effects.
We combine rigorous physics-based simulation with machine learning acceleration for accurate, efficient dynamics prediction.
Protein structures are prepared with appropriate protonation states, glycosylation, and membrane embedding when relevant. Force field parameters are assigned and systems equilibrated.
Slow dynamical modes are identified through time-lagged independent component analysis (TICA) or manifold learning. CVs are validated for capturing the slowest processes.
Metadynamics, umbrella sampling, or adaptive bias methods are applied using designed CVs. Simulations continue until convergence of free energy estimates.
Long trajectories are discretized into microstates and clustered into macrostates using kinetic clustering. MSM analysis provides thermodynamic and kinetic properties.
Predicted dynamics are compared against available experimental data. NMR relaxation, cryo-EM ensembles, and kinetic measurements confirm simulation accuracy.
Transition pathways, key residues, and mechanistic insights are extracted and visualized. Reports include trajectories, free energy surfaces, and residue-wise flexibility analysis.
From system setup to validated dynamics, our streamlined process delivers mechanistic insights.
Structure preparation, force field assignment, and system equilibration for dynamics simulations.
Identify slow dynamical modes, design collective variables, and validate sampling efficiency.
Run AI-accelerated MD with neural network-enhanced methods.
Markov state model construction, validation, and mechanistic interpretation of dynamics results.
Common questions about our AI-accelerated molecular dynamics services.
Our platform achieves effective sampling of microsecond to millisecond dynamics through neural network-enhanced sampling. This represents 10-100x acceleration over conventional MD, enabling access to biologically relevant timescales that are computationally prohibitive otherwise.
We study proteins, protein-protein complexes, protein-ligand systems, membrane proteins, and nucleic acids. Both explicit solvent (water/membrane) and implicit solvent simulations are supported depending on the scientific question.
We compare neural network-predicted dynamics against conventional MD benchmarks, NMR relaxation data, cryo-EM ensembles, and FRET measurements when available. Our methods are validated against established experimental observables before application to new systems.
Outputs include conformational free energy landscapes, transition pathways between states, kinetic rate constants, residue-wise flexibility analysis, allosteric communication maps, and ligand unbinding pathways with predicted kinetics.
Project timelines vary based on complexity and scope. We provide milestone-based deliverables so you can track progress throughout the engagement. Contact us for a detailed project timeline.
Our molecular dynamics services support research at universities, pharmaceutical companies, and biotech startups worldwide.
Our AI-accelerated molecular dynamics platform is grounded in peer-reviewed research on neural network potentials and enhanced sampling methods.
Gao, R., Li, Y. & Car, R. Enhanced deep potential model for fast and accurate molecular dynamics: application to the hydrated electron. Phys. Chem. Chem. Phys. 26, 23080–23088 (2024). https://doi.org/10.1039/d4cp01483a
Duignan, T.T. The Potential of Neural Network Potentials. ACS Phys. Chem. Au 4, 232–241 (2024). https://doi.org/10.1021/acsphyschemau.4c00004
Xu, M., Zhu, T. & Zhang, J.Z.H. Automatically constructed neural network potentials for molecular dynamics simulation of zinc proteins. Front. Chem. 9, 692200 (2021). https://doi.org/10.3389/fchem.2021.692200
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