A-optimal designs favor high-leverage connections to the reference ligand, as seen in the higher density of leverage points near 1 (Fig. ). From Eq. 24, the high leverage edges come at a cost where ligands far from the reference ligand are low leverage (Fig. ) and present in few cycles (Fig. ). As a consequence of the branched topology of A-optimal designs compared to the cyclic topology of D-optimal designs, the leverages of A-optimal designs are more varied -some edges are high-leverage, while other transformations weakly influence the regression. In contrast, Doptimal designs produce the lowest variance in 𝐻 𝑖𝑖 meaning that each edge exerts a more even influence on the regression and the residuals are more uniform (see Background, Eq. 6). As a result, D-optimal designs may compensate for the anticipated varied errors of transformations and provide more even coverage of the experimental design space.