The picture that emerges from our study can be summarised as follows: (1) for properties that depend mostly on the chemistry (such as the c p ), deep dreaming can produce pronounced shifts in the property distributions. For structural properties that are relatively easy to correlate (such as the VF), the deep dreaming approach also performs reasonably well. For more complex MOF properties that are sensitive to the configuration of atoms in 3D space (such as the heat of adsorption at infinite dilution), noSupplementary Table in the property distributions are observed. However, textual representations of MOFs alone, without positing atomic coordinate information, are less effective for optimising these properties within the deep dreaming approach; (2) a cornerstone of our model is its emphasis on interpretability, which is provided by attention score distributions learned during training, and further through inspection of the transmutation pathways charted by our model when tasked with a particular design objective; and (3) more work is required to guide property-driven optimisation of MOFs towards realisable structures, potentially by incorporating safety and synthesisability metrics as additional outputs in the multiobjective deep dreaming setting.