Despite the geometry optimization and low-energy conformer search, the RMSD remains fairly high. In some cases, this occurs because the lowest energy conforma-Table Effect of post-processing with MMFF94. The performance on 4548 molecules in the Platinum dataset is shown. Time column shows the total time to process all molecules in second. RMSD column shows mean RMSD in Å. Bond, Angle, Torsion columns show mean error of each ( Å, • , • ). TFD column shows mean of the torsion fingerprint deviation . Success indicates the percent of predicted molecules whose InChIKey match that of the original molecule. RMSD and mean error are calculated over successful molecules. tion is not necessarily the same as that in the experimental crystal structure . For example in Figure , the generated structure reflects an extended alkyl chain, the low energy conformation, but the experimental structure is folded. Generating geometrically diverse conformers is one method to find geometries matching such experimental pose. As stated above, performing a thorough conformer search is recommended to find either low-energy structures for modeling or diverse geometries to match experimental conformations found in crystal structures. Finally, we considered use of a larger fragment database by extracting fragments from all of the COD, Ligand Expo and the Platinum set itself. This larger fragments database includes 5,779 fragments, an additional 1,347 fragments more than the COD-only database, discussed above. Results are compiled in Table . In 9,741 fragments (>= 5 atoms), 9,004 (92.4%) were found in the rigid fragment database. The number of matches increased from 7,852 (80.6%) in case of using the CODonly database. This increase of matches reduced execution time (54.8 s to 28.1 s, respectively), since more fragments could be placed before searching general ring fragments. However, since almost all fragments were found in either of database even in case of COD-only database, the larger fragment data set did not contribute to increased accuracy.