Despite successful application in many areas, interpretability remains a challenge for deep learning-based methods 28 . PU-EPP uses attention mechanisms 31 to capture the importance and contributions of different input positions (e.g., atoms in substrates or residues in enzymes) to the final prediction, thereby inferring the knowledge the model has learned (Fig and). To verify whether PU-EPP was able to learn the catalytic mechanism behind the data, we mapped the attention weights to the substrates and enzymes and compared them with known data. Enzymatic catalysis lies in the enzyme's recognition of reaction sites on substrates. To evaluate the model's recognition ability for the reaction sites on substrates, we compared the attention weights with annotated reaction site data and found that ~60% of reaction sites were correctly identified, which was significantly higher than the results of random sampling . For instance, in screening tasks, the model successfully identified the oxygen atom near the reaction sites in ZEA and the nitrogen atom in OTA, which both are top-ranked in all atoms in ZEA and OTA .