Drug-target residence time (τ), one of the main determinants of drug efficacy, remains highly challeng-ing to predict computationally and, therefore, is usually not considered in the early stages of drug de-sign. Here, we present an efficient computational method, τ-random acceleration molecular dynamics (τRAMD), for the ranking of drug candidates by their residence time and obtaining insights into ligand-target dissociation mechanisms. We assessed τRAMD on a dataset of 70 diverse drug-like ligands of the N-terminal domain of HSP90α, a pharmaceutically important target with a highly flexible binding site, obtaining computed relative residence times with an accuracy of about 2.3τ for 78% of the compounds and less than 2.0τ within congeneric series. Analysis of dissociation trajectories reveals features that af-fect ligand unbinding rates, including transient polar interactions and steric hindrance. These results sug-gest that τRAMD will be widely applicable as a computationally efficient aid to improving drug resi-dence times during lead optimization.
This work describes an example of using τ-Random Acceleration Molecular Dynamics (τRAMD) in kinetic calculations.
The following tutorial describes how to run some of the calculations in this example: