While there is no way to deterministically interpolate between structures using this approach, one could argue that the stochastic nature of the deep dreaming process actually imbues it with a high level of physical interpretability. If we examine the individual transmutation pathways as in Figure , or consider the ensemble of decoded structures from the same seed as in Figure , we can garner insight into the model's understanding. For the particular case of VF, recovering appropriate design principles from the sequence of linker modifications proves to be straightforward and somewhat predictable: a longer linker typically results in a higher VF. This simple correlation manifests both in the individual transmutation pathways and in the collection of decoded structures. However, deciphering such correlations and extracting generalisable insights from machine learning models can often be complex.