Efficient Binding Free Energy Estimation with Coarse-Grained Funnel Metadynamics

Accurate estimation of protein–ligand binding affinities is essential for drug discovery but remains computationally expensive at the atomistic level. Here, we combine the Funnel Metadynamics (FMD) technique with the Martini3 coarse-grained (CG) force field to develop a Coarse-Grained Funnel Metadynamics (CG-FMD) framework for efficient binding free-energy prediction. Using colchicine binding to Colchicalin and BRD4 as test systems, we evaluate the effects of different CG backbone models and funnel geometries, demonstrating that protein flexibility critically influences ΔG_bind, while funnel size has negligible impact. Compared to all-atom FMD, CG-FMD achieves 25-fold higher simulation efficiency and captures 50% more binding events per microsecond, with significantly reduced computational cost. Preliminary applications to the tubulin αβ-heterodimer confirm the method’s ability to reproduce experimental binding affinities, highlighting CG-FMD as a scalable and reliable approach for studying complex protein–ligand interactions.