ReLeaSE uses a stack-augmented Recurrent Neural Network to produce chemically reasonable SMILES strings and was trained using a dataset of approximately 1.5 million drug-like molecules extracted from the ChEMBL22 database to generate chemically valid molecules. After this initial training, the generator can be further trained iteratively using Reinforcement Learning using an appropriate reward function to bias the generation towards molecules with the desired properties.