Accurate Physics-Based Flexible Docking of Macrocyclic Ligands

Macrocycles are an increasingly relevant class of therapeutics,
but their size and highly constrained conformational space poses unique
challenges to computational modeling. Existing modeling protocols
struggle to accurately sample macrocycle bioactive conformations and
predict their binding mode to receptors of interest, hindering structure-based
drug discovery campaigns. To address these limitations, we developed
a new flexible macrocycle docking protocol based on existing Schrödinger
technologies for macrocycle sampling and small molecule docking. To
benchmark its accuracy, we collected 240 diverse receptor-macrocycle
systems, expanding and improving over previously published data sets.
Our results show that our method improves significantly over existing
docking tools, recapitulating the receptor-bound structure in the
top 2 ranked poses for 82% of cases, including several clinically
relevant compounds. Finally, we discuss the limitations of our method
and the remaining challenges of macrocycle modeling, such as induced-fit
effects, strain, and the accurate placement of chemical decorations
around the macrocycle core.