In order to compare the readily generated and fixed molecules to the training set, evaluation metrics from the GuacaMol distribution-learning benchmarks and MOSES benchmark were used . The uniqueness of generated molecules was defined as the fraction of unique canonicalized SMILES in a sample of 10 000 valid sequences. The novelty was determined by the percentage of molecules in a set of 10 000 generated, valid molecules that were not present in a set of 100 000 reference molecules. In order to assess the similarity, the similarity to nearest neighbor (SNN), fragment similarity, and scaffold similarity were determined following the same approach as in the MOSES benchmark . Similarity to nearest neighbor (SNN) was calculated by taking the average of the Tanimoto similarity between 1 024 bit ECFP4 fingerprints of the generated molecules and their most similar neighbor molecules from the reference set. For the fragment similarity, the frequency of BRICS fragments was compared. For the scaffold similarity, the same was done based on the Bemis-Murcko scaffolds. For all the similarity metrics 10 000 generated molecules were compared to reference sets of 100 000 molecules. Lastly, the Kullback-Leibler (KL) divergence of the physicochemical property distributions of generated molecules and the reference set was calculated for the following properties: Bert's complexity index (BertzCT), octanol-water partition coefficient (logP), molecular weight (MW), topological polar surface area (TPSA), number of hydrogen bond donors and acceptors, number of rotatable bonds, number of aliphatic and aromatic rings and fraction of sp 3 hybridized carbons (CSP3) . The distribution of the continuous descriptors was represented by a smoothed histogram to allow for fast comparison of large sets (Supporting information Figure ). The KL divergences (DKL) of the different properties (i) were aggregated into one score following the method from GuacaMol using;