Since enzymes are complexes composed of hundreds of amino acid residues, identifying critical residues in enzymes is more challenging. Hence, we took ZH4 and OH1 as examples and analyzed their critical residues using molecular dynamics (MD) simulations and compared them with the important residues inferred through attention mechanisms. The most likely enzyme-substrate binding pose was predicted via systematic docking plus extensive MD simulations. Energetically critical residues that contribute significantly to substrate binding free energy were identified via the molecular mechanics-generalized Born and surface area (MM-GBSA) methods (Fig S7 ). We found that the attention weights of the interaction layer that has learned both enzyme and substrate features could map with the binding sites well, while the attention weights of the encoder that only learned enzyme features could not . This indicates that both enzyme and substrate features are crucial for prediction, which validated our initial methodological assumptions. It also demonstrates that although the model only takes enzyme sequences as input, it learned the hidden three-dimensional structural information (i.e., sites at which the substrate might bind). Furthermore, allosterically critical residues were calculated using root mean square fluctuation (RMSF) and dynamics cross-correlation map analysis (Fig S11 and). The consistency of important residues identified via attention mechanisms and MD (~90% on ZH4 and ~40% on OH1 were consistent) was significantly higher than that between MD and randomly selected residues (Fig 5E , and Fig S13 ), indicating that PU-EPP learned the meaningful enzymatic knowledge behind the data.