ΔGQB and alchemical transformations are comparable when predicting ΔGbind of azole-based HSP90 inhibitors. We started by investigating the power of DUck in rank-ordering a set of isoxazole-, inverse isoxazole-, and pyrazole-based HSP90α inhibitors. HSP90α has several properties that make it ideal for our method. Firstly, this HSP90α data set includes both thermodynamic and kinetic data (SPR and ITC data from Schmidtke et al., and personal communication by James Murray, Vernalis Ltd.). Secondly, the robustness of the essential interaction with D93 is an excellent predictor of binding and has been successfully used in DUck-based virtual screening. Finally, there is precedence with this system for using short SMDs for predicting and explaining the kinetic behavior of ligands.