We evaluated all the metrics described in Section 2.6 for the 821 compounds in the TTD database. The results in Table clearly show that the binary and count versions perform approximately the same in terms of Validity, Uniqueness, and Top Identical. The models with regularization term (λ = 10) significantly improve the Top Identical, Rank Score, and Correlation metrics. Finally, the model trained on D c with λ = 10 outperforms all the other models on the Rank Score, and Correlation metrics, showing that the ECFP4 fingerprint with counts achieves superior results compared to the binary version. The models were trained on RDKit canonicalized SMILES strings, however, the transformer models can also generate non-canonicalized valid SMILES strings. The uniqueness after the molecules are canonicalized, remains close to 1.0 in all cases, indicating that in most cases the canonicalized version of the SMILES string is the only one generated. However once stereo-chemical information is removed (NS in Table ), the uniqueness falls to 0.5 -0.6, which suggests that the transformer models are generating approximately two stereo-isomers for most of the target molecules. The increase from 0-0.3 to 0.6-0.9 for Top Identical when removing stereo-chemical information indicates that the transformer models generate different stereo-isomers with similar precedence.