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+# 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 
+