A software framework for Molecular Dynamics-based discovery
ACEMD was the first biomolecular code to run on GPUs. Started in 2006 with Acellera itself, it is now the commercial version of OpenMM.
As fast as possible Molecular Dynamics
- Speed: Designed and engineered with simulation speed in mind.
- Suited for drug discovery: It has been used to study protein-ligand binding, conduct small virtual screening campaigns or sample structural changes in large proteins.
- PLUMED integration: Run metadynamics with its PLUMED plugin.
- Technical support: Our team’s expertise is just one click away.
- Extensive documentation: Available here
- Easy to install:
conda install -c conda-forge -c acellera acemd3
- FREE: Running ACEMD in just one GPU is free for all.
“Until and unless other suites emerge that are as GPU-enabled, the ideal simulation technology at present is ACEMD on GPUs.”
Godwin, R. C., Melvin, R., & Salsbury, F. R. (2015)
Molecular Dynamics Simulations and Computer-Aided Drug Discovery (pp. 1–30). https://doi.org/10.1007/7653_2015_41
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.
Adding small molecules to the mix
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.
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.
You can read the related article here.