Torsional Diffusion ranks second, with values of 53.63 and 77.55 kcal/mol for TDdrugs-pg and TDdrugs modes, respectively. Torsional Diffusion exhibited some notable high-energy conformations of the larger molecules, specifically NAD, NAP, FAD, COA, and ACO. Further inspection revealed that while the energy calculations converged, some of the geometries were physically unrealistic (Figure ). It is noteworthy that despite employing MMFF94s force field optimization, Torsional Diffusion still generates conformers with a few geometric issues. This observation suggests that the MMFF94s optimization step is insufficient to detect or rectify the unrealistic conformations produced by the AI model. Interestingly, Conformator and RDKit generated conformers with the highest energy among the evaluated algorithms. One might initially assume that these higher energy values correspond to experimental structures due to strain, particularly when considering Conformator's ability to reproduce such structures. However, it is crucial to note that energy calculations encountered two types of failure: those due to unrecognizable topology due to issues in the initial geometries and those resulting from energy convergence issues, also, likely stemming from geometrical errors. Table quantifies the amount of calculation failures for each molecule. The larger, more complex molecules-NAD, NAP, FAD, ACO, and COA-accounted for most of these failures. Among the evaluated algorithms, RDKit exhibited the highest number of total failures (5870), followed by Conforge (3480), and Conformator (2100). ConfGenX, Torsional Diffusion, and Moltiverse demonstrated significantly fewer failures, with 80, 8, and 4 total failures, respectively. This outcome demonstrates that the quality of the conformers should be considered when evaluating conformer generation, as some geometric optimization strategies are not enough to ensure structural correctness, which inadvertently diminishes the usefulness of the conformer ensemble. Systematic quality ranking of conformer generation algorithms. The quality of the conformer generation algorithms was evaluated using eight distinct metrics: RMSDmin, RMSDstd, RMSDlow, RMSDhigh, RMSD-RDGYRcorr, RDGYRrange, RMSDmatrix, and ENERGY. These metrics collectively assess various aspects of conformational accuracy, diversity, and correctness.