The robustness of XAI methods was examined from three different perspectives. The first was to analyse XAI methods in the same circumstance, namely the same model and the same input, to see how different the explanation given is over all methods. Figure shows the different attributions from each XAI method for one randomly chosen canonical SMILES of the encoder-decoder ME2C model. Secondly, we want to investigate the variance that internal representation gives. To further illustrate this, the heat map shows the inner variance of the method, and the robustness score is the mean value of this heat map. Figure shows how the different training regimens for an encoder-decoder architecture influence the explanations of IG for on randomly chosen canonical SMILES. Again, the heat map shows the internal consistency for in-between model robustness. Finally, we investigate how input variation all representing the same underlying ground truth, can change the given importance of IG for the encoder-decoder ME2C model. In Figure , we investigate the canonical SMILES and ten randomized SMILES and compare the internal robustness between the importance given to the atom indices. Importantly, we reshuffle the respective SMILES strings according to RDKit canonical atom order so that each character represents the same atom when compared. Firstly, we analyse the difference between SMILES of the same molecules for each method and model training (Figure ). Overall, the cosine distance between these samples is greatest in the IG, SHAP and AttGrads and AttCat. Additionally, the distances of the attention maps, rollout attention are lowest and most variable. The Grads value seem to have the highest variability in most models. All other methods are relatively similar in all models, with the exception of the R2C and MR2C pre-trained models, where all methods have increased cosine distances in the encoder-decoder models, but less pronounced differences in the encoder-only models.