Lastly, our fourth proposal helps users select what type of optimization to run. D-optimal designs provide more even coverage of the experimental design space and result in more cycles (Fig. ). On the other hand, the metric used in A-optimization, the trace of the covariance matrix (Eq 8), is solely informed by the anticipated variances in 𝚫 Ĝ values (Background). The minimization of variances results in A-optimal designs favoring more connections to the most similar ligands to the set and also produced the lowest average MSE of 𝚫 Ĝ (Fig. ). We also note that A-optimal designs take longer than D-optimal designs to reach a solution in HiMap. Therefore, if the time for optimization is a concern, we recommend solving for the D-optimal design.