OpenFF NAGL
A playground for applying graph convolutional networks to molecules, with a focus on learning continuous “atom-type” embeddings and from these classical molecule force field parameters.
Getting started
OpenFF recommends using Conda virtual environments for all scientific Python work. NAGL can be installed into a new Conda environment named nagl
with the openff-nagl
package:
mamba create -n nagl -c conda-forge openff-nagl
conda activate nagl
For more information on installing NAGL, see Installation.
NAGL can then be imported from the openff.nagl
module:
import openff.nagl
Or executed from the command line:
openff-nagl --help
Inference with NAGL
NAGL GNN models are used via the openff.nagl.GNNModel
class. A checkpoint file produced by NAGL can be loaded with the GNNModel.load()
method:
from openff.nagl import GNNModel
model = GNNModel.load("trained_model.pt")
Then, the properties the model is trained to predict can be computed with the GNNModel.compute_property()
method, which takes an OpenFF Molecule
object:
from openff.toolkit import Molecule
ethanol = Molecule.from_smiles("CCO")
model.compute_property(ethanol)
A toolkit for the generation of neural network models for predicting molecule properties. |