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As fast as possible molecular dynamics:
Speed: Designed and engineered for maximum simulation speed.
Integration: Metadynamics with PLUMED plugin and OpenMM.
Technical support is a click away and documentation available here.

Suited for drug discovery

Used to study protein-ligand binding, conduct small virtual screening campaigns or sample structural changes in large proteins.

Free for non-profit research

Several components of. the ACEMD platform are free for non-profit use. Commercial licenses are available for other uses. Check our software.
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The ecosystem for high-throughput molecular dynamics

A powerful, open-source Python library for computational chemistry and structural biology.
Manipulate molecules: Perform all kinds of operations to your molecules with a couple of functions.
Run molecular dynamics: Prepare, build, run and analyze simulations through its integration with ACEMD.
Benefit from our experience: Documentation and tutorials available at Full source code available at our Github repo.

Small molecules

While forcefield parameters are readily available for proteins, parameters for small molecules must be computed. Parameterize is Acellera’s force field parameterization tool, which works in 4 easy steps:
Input your molecule as a .mol2 or .sdf file.
Choose between forcefields.
Choose between quantum, machine-learning or heuristic-based rules to compute parameters.
Done! Get your results in the form of a .frcmod file.
An online GUI for Parameterize is available at PlayMolecule.
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Dashboard mockup

The framework for differentiable chemistry and biology

TorchMD provides a simple to use API for performing molecular dynamics using PyTorch. This enables researchers to more rapidly do research in force-field development as well as integrate seamlessly neural network potentials (NNPs) into the dynamics, with the simplicity and power of PyTorch. TorchMD has already been proven capable of:
Deriving force field parameters from a short MD trajectory.
Creating a coarse-grained model for an arbitrary protein using NNPs.

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