While it is true that the dataset used for training our machine learning models could be biased and not structurally homogeneous enough, we took several steps to address this potential issue. First, we carefully curated the dataset to include only high-quality experimental data with wellestablished and accepted literature sources. Secondly, we performed rigorous cross-validation to evaluate the generalizability of the model to unseen data. Thirdly, we used feature selection techniques to identify the most informative features that contribute to binders' probability and selectivity towards specific E3 ligases. Finally, we validated the model on an independent test set and observed convincing performances, indicating that our model was not simply memorizing the training data. We are aware of the dynamic nature of the field: each novel ligase ligand should be added to the training set to improve generality of the model, so we are constantly keeping track of changes to enrich the training set and to provide the community with novel tools. As a future prospect, a classification of E3 ligases through their druggable cavities extracted, for instance, either from cited ELIOT database or from an Alpha Fold collection of Ligase 3D models could represent the natural playground to apply our predictions.