Benchmark dataset. The compiled dataset of organic small molecules, referred to as Cofactorv1 hereinafter, comprises seven biologically significant cofactors chosen for their prevalence and structural complexity. Table shows the summary of key properties of the dataset for each molecule. The dataset contains hundreds and thousands of high-quality, experimentally determined unique conformers per molecule in the bound state. Figure shows the distribution of the RSCC values and resolutions of the Cofactorv1 structures, which reveals a relatively homogeneous distribution of these properties across the dataset, indicating consistency in the quality of the experimental structures. The RSCC is a metric that quantifies the agreement between calculated and observed electron density maps . The chosen cofactors exhibit a wide range of molecular size and flexibility, where the number of total atoms varies from 47 (ATP) to 89 (ACO), the number of heavy atoms varies from 31 (ATP) to 53 (FAD), and the number of rotatable bonds range from 15 (ATP) to 29 (ACO). This dataset offers a complementary approach for evaluating conformer generators compared to widely used benchmarks like the Platinum diverse dataset , which contains 2859 different ligand structures with 10 or more heavy atoms and 1 to 16 rotatable bonds. The Geometric Ensemble of Molecules (GEOM) dataset comprises two subsets: GEOM-QM9, containing conformers of approximately 133,000 unique molecules, and GEOM-DRUGS, with conformers of about 317,000 unique molecules. The Platinum diverse dataset exhibits an average of 6.6 ± 3.6 rotatable bonds, while GEOM-QM9 has 2.2 ± 1.6, and GEOM-DRUGS shows a similar pattern to the Platinum diverse dataset with 6.5 ± 3.0 rotatable bonds. In contrast, the Cofactorv1 dataset contains very few but highly flexible molecules (7) and 8412 structures, showing a substantially higher average of 19.9 ± 3.5 rotatable bonds. The Cofactorv1 dataset intentionally incorporates molecules with many rotatable bonds as it reflects on the understanding that conformer generation becomes increasingly difficult as the number of rotatable bonds increases , thus providing a more stringent test of the generation capabilities. In previous studies, the accuracy of conformer algorithms has been defined as the minimum RMSD value between any structure of the generated ensemble, and a single experimental structure for every tested molecule . B MMFF94s parameter set . c MMFF94s_RTOR excluding electrostatic interaction terms 82 . d MMFF94s using a reparameterization for torsion parameters . e OPLS3e force field. The Cofactorv1 dataset does not have macrocycles. f GAFF2 v2.20 available in AmberTools23 was used. ). ConfGenX and Conformatorbest are slightly worse with RMSDmin of 2.34 ± 0.19 Å and 2.42 ± 0.15 Å, respectively. RDKit showed the worst accuracy in any of the three modes tested, where it produced large variability in the RMSDmin (low precision). This outcome is more pronounced for larger molecules. The greater number of outliers in the RMSDmin of the ACO molecule evidenced that some experimental structures could not be reproduced by RDKit (Figure , Table ). For the second largest molecule, COA, the best generators in terms of RMSDmin were Moltiverse, CONFORGEsys-def and ConfGenX with RMSDmin of 2.07 ± 0.19 Å, 2.11 ± 0.26 Å and 2.2 ± 0.19 Å respectively. Same as in ACO, RDKit gave the worse results: 2.81 ± 0.67 Å, 2.75 +-0.68 Å, and 2.71 +-0.64 Å for RDKitkdg, RDKitetkdg3, and RDKitetkdg3-mf respectively. FAD is the third largest molecule and the one with the largest number of experimental structures (2782). This is a special case where it can be observed that both accuracy and precision are complementary and more valuable than accuracy alone. The three algorithms with the best accuracy without considering precision are: Conformatorbest, RDKitetkdg3 and RDKitkdg with RMSDmin of 1.74 ± 0.15 Å, 1.92 ± 0.50 Å and 1.97 ± 0.48 Å respectively. Again, RDKit showed the lowest precision, where some structures could not be predicted correctly even within an RMSDmin of 4 Å (Figure ). Considering both accuracy and precision, Conformatorbest still is in the first place, followed by Moltiverse and Conformatorfast with RMSDmin of 1.99 ± 0.21 Å and 2.04 ± 0.17 Å respectively. Note that for the FAD molecule, the average RMSDmin values are beginning to be below 2 Å, which has traditionally been established as the cut-off for acceptable prediction . For the NAP molecule, RDKit gives values with higher accuracy, 1.71-1.74 Å, but with a lower precision compared to Conformator and Moltiverse, exhibiting outlier values up to 3.66 Å. The other best algorithms are ordered as: Conformatorbest, Conformatorfast, CONFORGEsys-best, Moltiverse, CONFORGEsys-def, Torsional diffusion and ConfGenX. For the NAD molecule, the behavior is similar to NAP, where RDKit has a high accuracy but with large outliers, and CONFORGEstochastic has the lowest accuracy (RMSDmin of 2.81 ± 0.24 Å). The algorithms with the best combination of accuracy and precision are Moltiverse and Conformatorbest with RMSDmin of 1.81 ± 0.17 Å, and 1.85 ± 0.17 Å respectively. For the smaller molecules, GTP and ATP, all algorithms reproduced the experimental structures with average RMSDmin values below 2 Å, which is expected from how well the algorithms have been calibrated for molecules with few rotatable bonds (<15). In contrast, the sampling based on the RDGYR done by Moltiverse seem to work equally good for both small and large molecules. In addition, the distributions of the number of experimental conformations predicted at various RMSD thresholds is illustrated in Figure . For smaller molecules, the distributions are steeper with 100% of the conformers reproduced at an RMSD less than 2 Å. However, these curves shift towards high values as the molecular size increases. Moltiverse demonstrates consistency across all molecules, exhibiting a sigmoid-shaped curve in all cases. In contrast, other algorithms show, at least for one molecule, curves deviating from the sigmoid shape with abrupt steps in their ascent. This pattern suggests that Moltiverse maintains a more uniform behavior across the tested cofactor molecules. Conformational diversity. Conformational diversity was assessed using two approaches, RDGYR range and RMSD matrix measurements for the generated conformers. The RDGYR indicates how compact (smaller value) or extended (larger) is a given structure, and it is particularly useful to evaluate the extent of the RDGYR (RDGYRrange) to compare the different algorithms (Figure ). The ensemble generated by RDKit (in any mode) showed a distribution of the RDGYR much narrower than the experimental structures (¡Error! No se encuentra el origen de la referencia.), especially for the larger molecules ACO, COA, and FAD. RDKit does not fully cover the experimental RDGYR distribution, which can be related to the poor accuracy of RDKit for these molecules. From these results, it can be suggested that RDKit tends to generate extended molecules. Conformatorfast, Conformatorbest, and Moltiverse showed the highest global RDGYRrange values: 2.71, 2.62, and 2.52 Å, respectively (Table ), which indicates that these algorithms are able to cover a higher diversity of compact and extended molecules. This also shows their potential to generate structures outside the training datasets. However, RDKit, CONFORGE, and Moltiverse produced the most extended structures unlike Conformatorfast. Although RDKit had the lowest precision in reproducing the structures for FAD (RMSDstd of 0.48-0.50 Å), it presented a higher average accuracy (RMSDmin). The latter may be related to the ability of RDKit to generate structures in a range of RDGYR closer to the highly-extended experimental structures (Figure ). As RDKit has a tendency for generating extended conformers, they mostly fit the experimental RDGYR range. For the small molecules, GTP and ATP, all generators covered the RDGYR ranges of the dataset, and this is reflected in the better accuracy overall.