Deep learning algorithms hold the promise of further accelerating advancements in almost every aspect of scientific research. Recent architectural developments in Deep Neural Networks (DNN) allowed for new applications beyond its initial targets in image recognition and text processing. As evident from the recent boost in the number of relevant publications , the field of chemistry has proved a fruitful ground for the application of the algorithms originally developed for Natural Language Processing (NLP)Hedderich et al. and Graph Processing (GP) purposes. Chemical objects emerged as the natural extension of these algorithms due to the common representation of molecules as SMILES (text representation) or molecular graphs (from valence theory). Both NLP and GP methodologies provide predictive and generative capabilities, thus paving the way for tackling the challenging problems of predicting molecular structures based on the desired chemical property.