Understanding Binding Dynamics in Host-Guest Systems with Advanced Sampling Simulations

Host-guest complexes are useful for applications in biomaterials science and engineering, including drug delivery, separations, transport regulation, and novel hydrogels. Highly-specific binding interactions are a key feature of such systems, and as such knowledge of systems' binding affinity, free energy landscapes, and binding pathways is critical. Here, we propose that advanced sampling techniques, such as the Adaptive Biasing Force and Artificial Neural Network methods, are promising ways to explore free energy landscapes of host-guest systems due to their ability to quickly sample the full phase space. We apply these methods to calculation of binding affinities between an array of small molecules and curcurbit[n]uril hosts. These binding affinities calculated with advanced sampling methods are compared to previously-published experimentally- and computationally-obtained values and obtain exceptional agreement with each. We also discuss how such methods might be generalized to more complex systems of interest in the biomolecular and materials simulations communities.