Our approach can be used with any continuous representation of the chemical space, such as those found in . In this study we build upon the continuous molecular representation framework reported earlier . This molecular representation was learned using a Deep Neural Network to translate from one molecular string representation (e.g. SMILES) to another. In this way, the model learns the common "essence" between both syntactically different string notations, i.e. the molecule which both notations are representing. By introducing a bottleneck in the architecture of the neural network, the molecule is encoded in a compressed embedding, that can be utilized as latent representation of the chemical space. As the model is trained on a huge dataset of approximately 75 million chemical structures stemming from various sources, the resulting latent space represents a wide range of the chemical space that can be explored. In this earlier work, we also showed that the learned molecular representation can be utilized as powerful molecular descriptors for quantitative structure activity relationships (QSAR) models. Moreover, transformations in the latent space result, if decoded back, in smooth transformations in the discrete chemical space in regard of structural as well as molecular properties. The interested reader is directed to the original publication for technical details of our framework .