Abstract: The dynamic nature of protein structures and the diversity of protein binding pocket dynamics provide challenges and opportunities for ligand design . We have developed TRAPP, a toolbox of computational approaches to identify TRAnsient Pockets in Proteins for ligand design. I will present recent developments in TRAPP to identify pocket conformations with high druggability. Protein binding site flexibility is one of the factors that can affect drug-target binding kinetics. Growing evidence that the efficacy of a drug can be correlated to target binding kinetics has led to the development of many new methods aimed at computing rate constants for receptor-ligand binding processes , see also: kbbox.h-its.org. Here, I will describe our studies to explore the determinants of structure-kinetic relationships and to develop computationally efficient methods to estimate drug-target binding kinetic parameters. I will introduce our τ-random acceleration molecular dynamics simulation (τRAMD) method to compute relative residence times  and discuss how machine learning analysis of τRAMD trajectories  and the application of Comparative Binding Energy (COMBINE) Analysis  can be used to decipher the determinants of drug-target residence times.