We experiment with four single-task data sets from MoleculeNet : BBBP (2K compounds) and BACE (1.5K) for binary classification, ESOL (1.1K) and FreeSolv (0.6K) for regression. To generate molecular representations from SMILES strings, we calculate 208 RDKit descriptors and extendedconnectivity fingerprints with radius 2 and 2048 bits . Next, we standardize numerical values and combine the obtained features to pass into one of the models from Figure � � � � � (note that depending on the distribution of a target property, we may use a different loss function for the empirical risk in [� � �] including huber, hinge, or log-loss).