There has been a recent surge of interest in the application of Deep Neural Networks (DNNs) to the de novo design of molecules with desired properties. DNNs are used to create generative models based on training data, and these models are used to create new molecules. One approach, introduced by Gómez-Bombarelli et al , trains a Variational Auto Encoder (VAE) that maps SMILES strings (Weininger ) into a high-dimensional latent space; after this, points in that space, typically found by an optimization or search procedure, may be decoded back to SMILES (for example, Kusner et al ). An alternative approach is to build the model using a Recurrent Neural Network (RNN) with Long Short Term Memory (LSTM) (Segler et al , Ertl et al , Olivecrona et al ) and generate SMILES using this probabilistic model. However, there is no guarantee that the resulting SMILES strings will be valid or even if they are, that they will represent a reasonable molecule structure. The percentage valid SMILES generated that represent reasonable molecules has been reported as ranging from 7% to 80% . These values can only be interpreted in the context of the diversity and relevance of the molecules generated (nor is it always clear what is meant by "valid molecule" or "valid SMILES"), but nonetheless illustrate the difficulties encountered.