  Molecule generation is a task made very difficult by the complex ways in
which we represent molecules computationally. A common technique used in
molecular generative modeling is to use SMILES strings with recurrent neural
networks built into variational autoencoders - but these suffer from a myriad
of issues: vanishing gradients, long-range forgetting, and invalid molecules.
In this work, we show that by combining recurrent neural networks with
convolutional networks in a hierarchical manner, we are able to both extract
autoregressive information from SMILES strings while maintaining signal and
long-range dependencies. This allows for generations with very high validity
rates on the order of 95% when reconstructing known molecules. We also observe
an average Tanimoto similarity of .6 between test set and reconstructed
molecules, which suggests our method is able to map between SMILES strings and
their learned representations in a more effective way than prior works using
similar methods.
