Computational Workflow to Unravel the Structural Dynamics of Supramolecular Metallacages in Solution

The structural dynamics of self-assembled metallacages is important because it determines their function and stability in different applications involving encapsulation and release of a guest molecule. We present here an integrated computational workflow to study the dynamic behavior of selected [Pd₂L₄]⁴⁺ metallacages in explicit solvents (water and DMSO) and benchmark them for future <i>in silico</i> investigations of their host-guest chemistry, pivotal to their application as drug delivery systems. Two different pathways for the Molecular Dynamics (MD) simulations of the systems are explored, namely classical force field (FF) and Machine Learning Interatomic Potentials (MLIPs), to assess the conformational changes of two cage systems in solution, enabling evaluation of the performance vs computational cost for both methodologies. The proposed workflow offers a versatile framework to computationally assess the structural dynamics of supramolecular systems in solution, effectively bridging the gap between quantum-level accuracy and the temporal and spatial scales needed for simulations of different functional applications.