Growing evidence that the efficacy of a drug can be correlated to target binding kinetics has led to the development of a plethora of new methods aimed at computing rate constants for receptor-ligand binding processes. Many of these are described in KBbox (kbbox.h-its.org), a toolbox of computational methods for studying the kinetics of molecular binding that includes tutorials. Here, I will describe the τ-random acceleration molecular dynamics simulation (τRAMD) method to compute relative residence times.  I will explain how interaction fingerprints, using MD-IFP, and machine learning can be used to analyse τRAMD trajectories to explore protein-ligand dissociation mechanisms and decipher the determinants of drug-target residence times. Insights obtained from recent applications of τRAMD, e.g. to study the kinetic selectivity of kinase inhibitors, will be discussed.

Protein-ligand dissociation rates and mechanisms from τ-random acceleration molecular dynamics simulation

ACS Spring Meeting 2021

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