SELFIES Example: Variational Autoencoder (VAE) for Chemistry

An implementation of a variational autoencoder that runs on both SMILES and
SELFIES. Included is code that compares the SMILES and SELFIES representations
for a VAE using reconstruction quality, diversity, and latent space validity
as metrics of interest.

Dependencies

Dependencies are pytorch, rdkit, and pyyaml, which can be installed
using Conda.

Files

• chemistry_vae.py: the main file; contains the model definitions,
  the data processing, and the training.
• settings.yml: a file containing the hyperparameters of the
  model and the training. Also configures the VAE to run on either SMILES
  or SELFIES.
• data_loader.py: contains helper methods that convert SMILES and SELFIES
  into integer-encoded or one-hot encoded vectors.

Tested at:

• Python 3.7

CPU and GPU supported

For comments, bug reports or feature ideas, please send an email to
mario.krenn@utoronto.ca and alan@aspuru.com