Rationalizing the “Diels-Alderase” Activity of Pd2L4 Self-Assembled Metallocages: Enabling the Efficient Prediction of Catalytic Scaffolds

Self-assembled cages have emerged as novel platforms to explore bio-inspired catalysis. While many different size and shape supramolecular structures are now readily accessible, only a few are proficient catalysts. Here we show that a simple and efficient DFT-based methodology i is sufficient to accurately reproduce experimental binding affinities (MAD = 1.9 kcal mol-1) and identify the catalytic and non-catalytic Diels-Alder proficiencies (>90 % accuracy) of two homologous Pd2L4 metallocages with a variety of substrates. We demonstrate how subtle structural differences in the cage framework affect binding and catalysis, highlighting the critical role of structural dynamics and flexibility on catalytic activity. This flexibility manifests in a smaller transition state distortion energy for the catalytic cage compared to the inactive structure. To facilitate the computational exploration of novel Pd2L4 systems, we introduce an open-source Python module cgbind, which largely automates the screening of novel architectures.