The approach we propose is to transform a SMILES string to another syntax that conveys identical information but is more suited to machine-learning, a syntax we will refer to as DeepSMILES. Having converted the training set to DeepSMILES, these strings (and not the original SMILES) are then used as input to the machine-learning method. This process is then carried out in reverse to interpret the output of a generative model, by converting the generated DeepSMILES strings to SMILES strings for subsequent processing or display.