Recovery experiments demonstrate the strong robustness of PU-EPP. An independent test set consisting of 20,000 enzyme-substrate pairs was used for model evaluation, among which >60% of pairs had not been included in the EC system. The final PU-EPP model showed remarkable performance with a receiver operating characteristic area under the curve (ROC-AUC) of 0.985 and precision recall curve (PRC)-AUC of 0.988, indicating it can achieve reliable predictions on both well-studied and less-studied enzyme-substrate pairs. On the contrary, previous models for enzyme function prediction, e.g., EPP-HMCNF 17 and CLEAN 20 , can only apply to hundreds of common substrates involved in the EC system. PU-EPP's wide applicability domain enables it to serve as a more useful tool for the identification of new enzymes with specific catalytic activities. To evaluate the improvement of the new proposed ML framework, we also compared the PU-EPP with ML models in the drug discovery field that utilize both protein and compound features 28,33,34 . We re-trained and tested PU-EPP and other models on a dataset comprising 20,000 positive samples and ~10 times the number of unlabeled samples. PU-EPP showed the best performance, followed by TransformerCPI 28 , DeepConv-DTI 33 , and GNN-PT 34 (Fig ). We also tested the performance of PU-EPP on five independent small-scale datasets collected by , including data on phosphatase, esterase, glycosyltransferase, β-keto acid cleavage enzymes, and halogenase, that comprised real positive and negative enzyme-substrate pairs (Table ) 35 . We then compared the performance of PU-EPP with the best models reported in their study 35 . PU-EPP also showed better performance on each dataset, illustrating its superiority (Fig 3B ). Here, 1%, 3%, 5%, 10%, and 20% of positive samples were added to the unlabeled samples. PU-EPP successfully identified ~80% of mislabeled positive samples, which presented better robustness and anti-interference ability than the baseline models.