In order to fully assess the predictive performance of the QAPs, the achieved metrics were compared with predictive performances of a baseline and traditional chemoinformatics methods implemented in the RDKit Python module: Morgan fingerprints, atom pairs, topological torsions, RDKit fingerprints and molecular descriptors. Due to the high dimensionality of data and the possible presence of noise, generated features that showed a standard deviation less than 0.05 were dismissed.