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README.md

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 [email protected] and [email protected]