OpenMM-ML

This is a high level API for using machine learning models in OpenMM simulations. With just a few lines of code, you can set up a simulation that uses a standard, pretrained model to represent some or all of the interactions in a system.

Supported models and frameworks

In the current release, the supported potential functions are the following:

• ANI-1ccx and ANI-2x, using the implementations in TorchANI. They are suitable for small molecules involving a limited set of elements and no charges.

• MACE models, including the pre-trained MACE-OFF23 models, utilizing the MACE implementation. MACE-OFF23 models are suitable to describe intra- and intermolecular interactions of neutral closed-shell bio-organic systems.

• Models generated by NequIP via this implementation. Please note that you need to supply your own deployed model. Refer to README and example for more information. Models generated by Allegro are also supported by this interface.

Future releases will add new potential functions that support a much wider range of molecules.

Documentation

You can find the current documentation at https://openmm.github.io/openmm-ml.

Installation

The stable version of OpenMM-ML can be installed with conda or mamba.

mamba install -c conda-forge openmm-ml

We recommend using mamba, since it is faster and less buggy than conda.

Usage

To use this package, create a MLPotential object, specifying the name of the potential function to use. You can then call createSystem() to create a System object for a simulation. For example,

from openmmml import MLPotential
potential = MLPotential('ani2x')
system = potential.createSystem(topology)

Alternatively, you can use createMixedSystem() to create a System where part is modeled with this potential and the rest is modeled with a conventional force field. As an example, suppose the Topology contains three chains. Chain 0 is a protein, chain 1 is a ligand, and chain 2 is solvent. The following code creates a System in which the internal energy of the ligand is computed with ANI2x, while everything else (including interactions between the ligand and the rest of the System) is computed with Amber14.

forcefield = ForceField('amber14-all.xml', 'amber14/tip3pfb.xml')
mm_system = forcefield.createSystem(topology)
chains = list(topology.chains())
ml_atoms = [atom.index for atom in chains[1].atoms()]
potential = MLPotential('ani2x')
ml_system = potential.createMixedSystem(topology, mm_system, ml_atoms)