The dataset used to attempt the multi-class classification model is the largest so far reported. We found that all algorithms used for multi-class classification performed well as reported in The most performant models came from Simple Tree and XGBoost algorithm but we chose to focus on XGBoost only being more robust. We also extracted the most relevant ten ErG bits as reported in Figure . Only two out of those relate to distances more than 5, suggesting that close localized pharmacophores are more important than wider ones. Six out of ten relate to distances between hydrophobic groups (Hf) and acceptor (Ac) or donor atoms (D). In the ErG scheme, every group of three or more contiguous carbon atoms are generating Hf groups, even when located in aliphatic rings. Hf_D_d2 is surely present, among others, in hydroxy-proline moiety, while Hf_Ac_d2 is present, among others, in succinimide-like rings, but not in maleimide analogues. Interestingly, there is only one relevant ErG bit dealing with aromatic (Ar) groups, even if almost all the E3 ligands so far collected have at least one Aromatic group in them.