Despite the structural novelty of the generated molecules, their easily computable molecular properties (molecular weight, octanol-water partition coefficient, and numbers of hydrogen bond donors and acceptors) follow similar distributions as for the training molecules or all molecules in the ChEMBL dataset (Fig. ). This invariance of distributions is desirable, because with drug-like molecules as the input, the output of the model also stays drug-like (at least for the shown properties). This invariance is also expected, because in the training set, the assignments of which molecule is considered as an input, and which one as the output, were made at random, and therefore, did not create a bias towards higher or lower values of the listed molecular properties.