For supervised binary classification modeling, we employed PyCaret version 3.3.2 for Python to develop models using 15 different ML algorithms (Table in the Supporting Information). Each model was trained on ChEMBL data for the selected targets, associated with a binary activity label (active/inactive). Morgan Chiral of radius 2 (2048-bits) fingerprint and physicochemical descriptors were used as molecular representation, with PyCaret's default hyperparameter settings.