The cocamide RF regression model used the most fingerprints for the prediction of the r i indices (i.e., 215 fingerprints), while the UoA and amide r i models used 165 and 61, respectively. The low number of fingerprints used for amide was not surprising due to the fact that the compounds in this r i series are only comprised of C, H, N, and O. Hence, the amide r i model only used the 2DAPC fingerprint counts with a certain distance between C, N, and O atoms. At first sight, this was also noticeable when comparing the top 20 most important fingerprints for the three r i models (S3). The most contributing fingerprints for the amide r i model were the distances 1 till 7 between two C atoms with importance ranging between 27% and 4%. As for the UoA r i model, C-Cl and C-X distance begin to contribute more to the model and the most important fingerprint (i.e., distance 7 between C-C) only contributes 9.6%, having an overall more divided importance between a larger group of contributing features than the amide model. Finally, a similar trend was also observed for the cocamide model, except that the C-X distances start to play a more important role than the C-Cl distances, which could be explained by the higher number of halogens present in the compounds from the cocamide dataset. This variability in important features used in each r i regression model shows that different structures may be better captured by one r i model vs another, due to the diversity of training set in terms of chemical structures. This, also, further indicates the need for a more generic model incorporating the information from all three r i models.