On the other hand, performance of the random cross-validation fingerprints-based DL model (BEDROC(20) of 0.89) is slightly lower to the other published DL-based proteochemometrics model 28 (0.96). Lenselink et al represent proteins through standard physicochemical descriptors, whereas we use their amino acid sequence. The fact that amino acid-based representations attain a good predictive power poses the opportunity to gain insights into the mechanisms causing protein-ligand binding by analyzing biological patterns in the CNN filters and the long-range dependencies in the LSTM. Regarding the validation, Lenselink et al apply a temporal split strategy that drops the BEDROC metric 0.11 units, while our clustering strategy penalizes 0.33 units to our DL model. This is expected as time-split cross-validation can still su↵er from chemical series bias.