A Software Framework For Molecular Dynamics-Based Discovery
The ACEMD molecular simulation engine was the first biomolecular code to run on GPUs. Started in 2006 with Acellera itself. Since 2020, ACEMD and OpenMM joined forces to innovate and deliver the most advanced molecular dynamics tools. The platform includes:
ACEMD and OpenMM engines
HTMD, high-throughput molecular dynamics Python framework
Parameterize to obtain new paramters for small molecules
As fast as possible molecular dynamics:
Speed: Designed and engineered for maximum simulation speed.
Integration: Metadynamics with PLUMED plugin and OpenMM.
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.
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 software.acellera.com. Full source code available at our Github repo.
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.
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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