Conformator, in its best mode, is placed in the second rank with a RANK of 22, demonstrating superior accuracy and precision among the studied algorithms. The third position is shared by RDKitetkdg3-mf and Torsional Diffusion (TDdrugs), both achieving a RANK of 35. Conforgesys-def and ConfGenXdef-opls3e occupy the fourth and fifth positions, with RANK of 38 and 48, respectively. Figure shows the general quality of each algorithm's best mode across all metrics. This visualization is based on a normalization of each variable in a scale from 0 to 1, where 1 represents the most desirable value for each metric. Lower raw values are optimal for RMSDmin, RMSDstd, RMSDlow, RMSDhigh, RMSD-RDGYRcorr, and ENERGY, while higher raw values are optimal for RDGYRrange and RMSDmatrix. The plot shows how closely the algorithms approach each other in each dimension. A hypothetical ideal algorithm would achieve the optimal position across all metrics, resulting in a polygon that fully extends to the outer limits of the radar plot. It's important to note that the inclusion of additional methods in future comparisons could potentially alter the scale of this graph, as the normalization process relies on the extreme values observed for each metric. This approach would likely lead to more diverse and representative conformer ensembles. RDKit conformers exhibited the highest failure rate in energy calculations hinting at structural issues (Table ). Notably, the RDKitetkdg3-mf mode showed significantly fewer failures compared to the