• rewards for defined substructures (e.g. fixing a scaffold) or similarity to a certain compound These functions either work directly on the continous representation (e.g. QSAR models, similarity) or on the decoded SMILES representation utilizing the chemoinformatics library RDKit . In this study, we build two biological activity models for prediction of Epidermal Growth Factor Receptor (EGFR) and asparyl protease b-site APP cleaving enzyme-1 (BACE1) activity. These targets were arbitrarily choosen. Compounds with reported IC50 values were extracted from ChEMBL and preprocessed as described in . We encoded the molecules in the continuous representation and trained Support Vector Machine (SVM) regression models (as implemented in the Python library scikit-learn ) on predicting the IC50 values of the compounds. Moreover, we trained SVMs on solubility, metabolic stability and membrane permeability endpoints utilizing in-house data. SVM hyperparameters were optimized in a 5-fold cross-validation.