Following these results, low-level MMFF conformers and CREST conformers were tested using the full RMS clustering scheme mentioned above. Comparing each set of descriptors with each other, the CREST conformers do not offer a significant advantage over the MMFF conformers, despite having similar distribution patterns among the compounds (Fig ). In particular, the RDKit 3D descriptors may have contributed to the weaker than expected performance of CREST. Also, as various models were used to score the final conformer-based molecule groups, there was a greater drop in test accuracy from the training accuracy, which merits future attention. Based on the results, we reproduced the coefficients of the features represented in the best-performing models (Fig ). However, only some of these features displayed significant and consistent parity of coefficients between the two sets. In conclusion, Hall-Kier topological descriptors (Chi, Kappa) and a subset of the fingerprint descriptors emerged as the strongest predictors, although some functional groups like ester and quarternary carbon also displayed high coefficients.