Drug-target binding kinetics, as reflected through on-target residence time, is increasingly recognized as an important driver of in vivo drug efficacy and safety. However, due to long time scales and path dependency, computational estimation of residence times is still very challenging and as such has not yet been routinely  incorporated into drug discovery project execution. Here we present a novel enhanced sampling-based approach to computing residence times which combines automated selection of collective variables and ligand exit pathways with infrequent metadynamics to estimate the off rates. We report predicted residence times for over two dozen ligands across two pharmaceutically relevant protein targets, which are in good agreement with experimental results. The method is scalable, fully automated, and computationally efficient, with turnaround times comparable to free energy calculations routinely used to estimate drug binding affinities - thus allowing the workflow to be readily deployed in active drug discovery projects.

A high-throughput approach to computing drug-target residence times by combining enhanced sampling methods

ACS Fall 2021

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