Catalysis plays a pivotal role in chemistry. By lowering reaction barriers, catalysts facilitate chemical transformation under mild conditions, contributing to sustainable and economic processes. Nature has perfected this principle in enzymes, through precise control of the environment surrounding the catalytic centre and substrate. In an effort to mimic the tight control over the environment, the field of supramolecular catalysis chemistry has emerged. For example, the use of a macrocycle allows the substitution of rare earth metals with more abundant counterparts, while maintaining high catalytic performance. Complimentary to experimental advances, computational chemistry has played a key role in the design and understanding of catalytic systems, by elucidating reaction mechanisms. Despite considerable efforts, quantum chemistry is limited in its predictive abilities. As these systems grow in complexity, there is a need to improve not only on the underlying electronic structure theory used to study them, but also the chemical model that describes the catalytic system in its environment. Often, the errors introduced by a too simplistic chemical model exceed those arising from the use of an approximate theoretical methodology such as Density Functional Theory (DFT). Consequently, the goal is to create a chemical model which is a "digital twin" of the reaction flask, where the in-silico procedure can fully replicate experimental conditions, that is a catalyst in explicit solvent, at finite temperature and pressure. As a full ab-initio quantum chemical level is not feasible, a tailored multiscale strategy has to be developed, accounting for conformational flexibility, explicit solvation effects, and the dynamic nature of chemical reactions.