As it was hypothesized that error correction could also be used for the expansion of the nearby chemical space, synthetic errors were introduced and fixed for a set of compounds that were selective for AURKB over AURKA. 97 % of output sequences were valid, but they did have a low uniqueness (39 %) and novelty (between 16 and 37 %) compared to, for example, fragment-based analog generators . This was due to the fact that in most cases the original molecule was regenerated. In a recent study, Creanza et al. created a SMILES-based analog generator that works by sampling SMILES with substitutions . When using 5 substitutions, this approach has a lower validity (11 %) but higher uniqueness (60 %) compared to SMILES correction for exploration. On average the novelty when one altered version of an input sequence was corrected was around 37 % (Figure ). When more than 400 different random erroneous versions were created based on the same sequence the novelty decreased to around 16 %. These results, therefore, show that this method of exploration can be used to generate novel compounds, which will be explored further in the next section. 1.00 ± 0.00 0.41 ± 0.02 0.92 ± 0.12 0.30 ± 0.06 0.80 ± 0.23 Generated -train 1.00 ± 0.00 1.00 ± 0.0 0.39 ± 0.10 0.88 ± 0.18 0.40 ± 0.17 0.75 ± 0.28 The mean ± standard deviation for the 3 general generative model case studies is given. Uniqueness is the fraction of unique molecules in a sample of 10 000 valid molecules. Novelty is the fraction of 10 000 molecules that are not present in a sample of 100 000 from the reference set. For the similarity metrics, 10 000 molecules were compared to 100 000 molecules from the reference set. SNN is the similarity to the nearest neighbor. Fragment and scaffold similarity are calculated by comparing the frequency distribution of different fragments or scaffolds compared to the reference set. KL divergence describes the similarity of the physicochemical property distributions of 10 000 molecules compared to the reference set.