On the other hand, whilst DUck overestimated VER45862, which lacks a halogen at R1 necessary for hydrophobic complementarity, it provided excellent correlation for the remaining 8 molecules (r=0.69; ρ=0.83), correctly predicting that amides at R2 and the isoxazole ring led to significant potency boost (Figure ). Compared to alchemical transformations, DUck offers a major efficiency advantage, as each calculation is much faster and ΔGQB is only calculated once per ligand (Table ). It also predicts kinetic off rates better than equilibrium constants (Figure ). However, while alchemical transformations and other free energy methods have a sound theoretical foundation, this is not the case for DUck, which only samples configurations near the bound state and uses a small subset of the binding site (50 residues on average) to represent the receptor. Thus, it is particularly critical to determine its predictive power and applicability domain. High-throughput ΔGQB calculations can be used to accurately predict the formation of activity cliffs. Taking advantage of DUck's higher throughput, we investigated three systems, for a total of 683 ligands. Estimating that quantitative ΔΔGbind predictions may be a tall order for such a method, we ask whether ΔGQB can predict activity cliffs. Activity cliffs (AC) highlight structural motifs and groups of high relevance for a compound's affinity, and their early detection is crucial in drug discovery. To corroborate the results obtained on Hsp90α, we first explore an extensive AC dataset of the same system using data published by Kokh et al. The 93 inhibitors were paired by similarity and grouped by the receptor conformation they bind to; 13 bound to the loop conformation and 80 to the helical conformation, the latter leading to the opening of a cryptic site