# 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