Models were trained on the dataset generated from three di↵erent publicly available sources by Riniker and Landrum for true reproducibility and comparability of benchmarking studies. This dataset incorporates 88 targets from ChEMBL, the Directory of Useful Decoys (DUD) and the MUV. The selection of actives and decoys was conducted on drug-like molecules and in such a way as to cover the maximum range of the chemical spectrum, based on diversity and physical properties. ChEMBL and DUD decoys were selected from the ZINC database. The selection of ChEMBL targets was based on the 50 human targets and actives proposed by Heikamp and Bajorath study and performed on ChEMBL version 14. We only selected 500 decoys randomly from all those available for each target, in order to have a more computationally-approachable dataset and to decrease active/decoy imbalance per target while keeping a plausible proportion. The list of molecules identified by their SMILES was then standardized to avoid multiple tautomeric forms. Finally, these compounds were filtered to remove salts, those with molecular weight >900Da or >32 rotatable bonds and those containing elements other than C, H, O, N, S, P or halides.