Research work from 2018 proposed an approach based on a generative model that made use of a recurrent neural network for de novo drug design. The model was trained with a large molecular set from the ChEMBL database, which annotates biological activity data and chemical structures. With this training the model learned the grammar of SMILES, the chosen molecular representation for the molecules. To generate focused libraries the model was fine-tuned with active modulators of a specific target. This was another strategy that took advantage of bioactivity data to generate novel molecules.