In recent years deep learning has been a major addition in machine learning. Problems that were difficult to tackle before are now successfully approached using deep learning, such as image classification , face recognition or playing Go . Recently there has been another step forward in the field with deep generative models, which generate content similar to that upon which they have been trained. Deep generative models have been successfully applied to music composition , image generation [14] and language translation . These new methods are also being applied to chemical space exploration in a novel way . When trained with a small subset of molecules, these models generate molecules similar to the training set. Different types of neural networks such as Variational Auto-Encoders (VAE) , Recurrent Neural Networks (RNNs) and Generative Adversarial Networks (GAN) trained with string representations (SMILES) from ChEMBL have proven to be successful at generating novel chemical space.