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Metallocages offer a diverse and underexplored region of chemical space in which to search for novel catalysts and substrate hosts. However, the ability to tailor such structures toward applications in binding and catalysis is a challenging task. Here, we present an open-source computational toolkit, cgbind , that facilitates the construction, characterization, and prediction of functional metallocages. It employs known structural scaffolds as starting points and computationally efficient approaches for property evaluation. We demonstrate the ability of cgbind o construct libraries of cages with varied topologies and linker functionalities, generate accurate geometries (RMSD &lt; 1.5 Å to crystal structures), and predict substrate binding with accuracy on par with semiempirical QM, all in seconds. The cgbind code presented here is freely available at github.com/duartegroup/cgbind and also via a web-based graphical user interface at cgbind.chem.ox.ac.uk. The protocol described here paves the way for high-throughput virtual screening of potential supramolecular structures, accelerating the search for new hosts and catalysts.

cgbind: A Python Module and Web App for Automated Metallocage Construction and Host–Guest Characterization