Another way of understanding why randomized variants are better is to draw a parallel with image classification models. For example, when an image classification model is trained to predict whether an image depicts a cat, the model performance can be improved with a training set that has examples of cats from all the possible angles and not always a front picture. This is not always easy to obtain in image predictive models, but in the case of molecular generative models it is extremely easy to generate snapshots of the same molecule from different angles (i.e., different ways of writing the SMILES string). This allows models to better learn the constraints of the training set chemical space (i.e., heteroatom ratios, allowed functional groups, etc.). Nevertheless, for each molecule there is a different number of randomized SMILES (Figure ), thus possibly generating a bias towards the molecules that have more representations. None was detected in this study possibly because larger and highly branched molecules, which tend to have more combinations, are also generally more difficult to sample and can, in effect, counteract the bias (Figure ). Lastly, the restricted variant of randomized SMILES performed best, indicating that restricting the randomized SMILES algorithm makes the model generalize better. For example, the unrestricted randomized SMILES can represent the phenyl ring of aspirin (Figure ) in a much more convoluted way "c1cc(c(cc1)", something that would be impossible in the restricted variant. Finding variants that perform even better should be a future research goal in this field.