The computational workflow is outlined in Figure (bottom), with full details given in Section S7.1-7.2 of the Supporting Information. First, each precursor was input using their simplified input line entry system (SMILES) code, that contains information of atom and bond types only. A forcefield-level (MMFF) conformer search was then conducted, where the lowest energy conformer was then stored in a non-tabular NoSQL database (MongoDB) with a key of their corresponding canonical SMILES. The optimised molecule should best represent the most common conformer of the precursor, and therefore the one most likely to react in the subsequent imine condensation. Next, the cages were assembled using these optimised precursors. Specifically, we conducted 1464 molecular dynamics (MD) simulations corresponding to the four topologies that the 366 precursor combinations could potentially form (Tri 2 Di 3 , Tri 4 Di 6 , Tri 6 Di 9 , Tri 8 Di 12 ). We used the open-source supramolecular toolkit (stk) package to assemble each precursor combination into the chosen topology, connected through imine bonds. Then, a three-step optimisation process initially performed two forcefield (FF) optimisations on the assembled cage, firstly of the imine bond linkers, then on the rest of the cage atoms, to create a suitable input structure for an MD simulation using the OPLS3e forcefield, an extension of OPLS3 that has previously been used to accurately model imine cages. The MD simulation sampled conformers along the MD trajectory, which were then geometry optimised using the using the OPLS3e forcefield; the lowest energy conformer of the cage in the chosen topology was selected and stored in a 'constructed cage' MongoDB, alongside a key of its catenated canonical SMILES of its building block precursors. A well-organised and retrievable dataset was imperative for the streamlining of this workflow. This was repeated for all precursor combinations across all four topologies, resulting in 1464 modelled cages.