Minisymposium Presentation

Computational approaches for estimating protein-ligand binding affinities are crucial in modern drug discovery. All-atom explicit-solvent molecular dynamics (MD) simulations use the foundational principles of classical mechanics and statistical thermodynamics to rigorously calculate binding free energies. They are the most accurate affinity prediction methods that are still tractable when applied to biological systems, but they are also among the most computationally demanding. To mitigate costs, efficient sampling protocols such as multistate methods have been developed, enabling calculations of relative binding free energy across multiple ligands from a single simulation. One such technique, replica-exchange EDS (RE-EDS), leverages similarities in ligand environments to achieve improved sampling performance compared to pairwise methods. Its recent GPU-accelerated implementation in the OpenMM toolkit for molecular simulation has been benchmarked on a set of solvation free energies from the FreeSolv dataset and forms the basis of a high throughput framework for free energy calculations.