In order to create the target-directed RNN, the pretrained DrugEx model was fine-tuned on the molecules tested on AURKA and/or AURKB, respectively, and optimized towards selective AURKB compounds using the exploration network. To inform the exploration network of DrugEx and to predict the bioactivity of novel molecules, 3 prediction models were created; two models that were trained to predict the bioactivity on AURKA and AURKB, and one selectivity window model that predicts the selectivity for AURKB over AURKA. To this end, quantitative structure-activity relationship (QSAR) regression models were constructed using Scikit-learn . The molecules were described using 19 physicochemical properties and extended-connectivity fingerprints of the molecules, all calculated with the RDKit and the fingerprints with a radius of 3 bonds and folded to 2 048 bits (ECFP6).