The first step was to gather a dataset of known E3 ligase ligand complexes, which would serve as the training set for the machine learning model. This dataset was created by merging three PROTAC resources namely PROTAC-DB 2.0 , PROTACpedia () and a commercial subset of Evolvus's Liceptor database (), where the E3 ligase binding components of original active PROTACs are structurally identified and assigned. This yielded a total of 643 unique ligands. Additionally, we expanded the chemical space of E3 binders with 19 ligands specific to DDB1 and CUL4 associated factor 1 (DCAF1) which were not present in PROTACsderived collections . The summary of the dataset of unique 662 compounds alongside the seventeen E3 ligases targets is shown in Figure . Since certain target classes (i.e., DCAF1, DCAF15, DCAF11, and DCAF16, MDM2 proto-oncogene (MDM2), aryl hydrocarbon receptor (AHR), baculoviral IAP repeat containing 3 (cIAP2/BIRC3), ring finger proteins 4 (RNF4) and 114 (RNF114), fem-1 homolog B (FEM1B), ubiquitin-protein ligase E3 component n-recognin 1 (UBR1), and Cullin 4A (CUL4A)) had less than 20 compounds each, we clustered them together in a common class called "Other". This grouping was an approach to reduce the effect of imbalance on the E3 ligase set. As a result, we identified 6 target classes for the resultant 662 E3 ligase ligands. Next, we extracted the candidate pharmacophores for each ligand with help of the ErG pharmacophoric fingerprint as implemented within MOE 2022.02 () through the relative publicly available script (). The extracted pharmacophores were clustered based on similarity using methods such as hierarchical clustering or k-means clustering (see Figure in supplementary material). Once the pharmacophores were, they were used to generate a machine learning model using techniques such as random forest, XGBoost, or simple partition tree. The model would take the chemical structure of a new ligand as input and predict the most probable E3 ligase target for the corresponding ligand based on the similarity of its pharmacophore to those in the training set.