For the evaluation, the original digits were re-inserted into the SMILES strings predicted by the second model. The SMILES strings were canonicalised and the Tanimoto similarity based on PubChem fingerprints was computed using the CDK. The performance evaluation was done based on the average Tanimoto similarity, the proportion of Tanimoto similarity values of 1.0, the proportion of exact string matches based on the canonical SMILES and the proportion of valid predicted SMILES representations of molecules (Figure ). The model that was trained on the modified SMILES representation of Markush structures outperforms the model that was trained on the original SMILES representations. It yields a higher proportion of valid predicted SMILES strings (+3.4%), a higher proportion of Tanimoto 1.0 similarities (+2.2%) and a higher average Tanimoto similarity (+0.04), although the number of perfect predictions is slightly worse (-0.5%) for more details see Supplementary Table .