  Chemical autoencoders are attractive models as they combine chemical space
navigation with possibilities for de-novo molecule generation in areas of
interest. This enables them to produce focused chemical libraries around a
single lead compound for employment early in a drug discovery project. Here it
is shown that the choice of chemical representation, such as SMILES strings,
has a large influence on the properties of the latent space. It is further
explored to what extent translating between different chemical representations
influences the latent space similarity to the SMILES strings or circular
fingerprints. By employing SMILES enumeration for either the encoder or
decoder, it is found that the decoder has the largest influence on the
properties of the latent space. Training a sequence to sequence heteroencoder
based on recurrent neural networks(RNNs) with long short-term memory cells
(LSTM) to predict different enumerated SMILES strings from the same canonical
SMILES string gives the largest similarity between latent space distance and
molecular similarity measured as circular fingerprints similarity. Using the
output from the bottleneck in QSAR modelling of five molecular datasets shows
that heteroencoder derived vectors markedly outperforms autoencoder derived
vectors as well as models built using ECFP4 fingerprints, underlining the
increased chemical relevance of the latent space. However, the use of
enumeration during training of the decoder leads to a markedly increase in the
rate of decoding to a different molecules than encoded, a tendency that can be
counteracted with more complex network architectures.
