In this work, we extend the approach of Shen et al. to MOFs for linker optimisation, which is the area of MOF design with the highest capacity for customisation and therefore functional tunability. We first develop a bespoke chemical language model, augmented with attention mechanisms, to map string-based representations of MOFs to their property values.