Owing to the large training dataset used in this study, it was impossible for us to use common parameter search approaches, such as grid search or random search, to obtain the best hyperparameters. According to previously published studies and experimental tests on small datasets, we set the batch size to 30, the learning rate to 1E-4, the weight decay to 1E-4, the layers of the encoder block and decoder block to 12, the hidden dim to 128, and the norm shape to 128. The models were built on PyTorch 1.10 (). We used five NVIDIA Tesla V100 GPUs for model training, which required about 14 days. Model evaluation. PU-EPP was compared with TransformerCPI 28 , DeepConv-DTI 33 , and GNN-PT 34 , with their default hyperparameters. We also tested the performance of the PU-EPP with fivefold cross-validation on five independent datasets, including phosphatase, esterase, glycosyltransferase, β-keto acid cleavage enzymes, and halogenase (Table ), which were compared with the performance of prediction models developed by Goldman et al. 35 . To demonstrate whether PU-EPP can identify potential positive samples from unlabeled samples, we conducted recovering experiments of mislabeled positive samples on these five datasets. To this end, 1%, 3%, 5%, 10%, and 20% of the positive samples were mislabeled as negative, and the recognition capability of PU-EPP and default models on these mislabeled samples was tested with five-fold cross-validation. Prediction and evaluation of mycotoxin biotransformations. ToxinDB 13 was used to predict the potential biotransformations of mycotoxins. To rationally select the optimal biotransformation for detoxification of ZEA and OTA, we evaluated the feasibility, safety, and economics of predicted biotransformations at the molecular (e.g., the availability of co-reactants and toxicity and synthesis feasibility of mycotoxin metabolites) and reaction levels (e.g., reaction feasibility and biotransformation types) (Table ). Because ToxinDB 13 can only predict the main metabolic products of the input mycotoxins, the co-reactants of the top-ranked reference reaction provided by ToxinDB were considered as the co-reactants of the predicted biotransformation and subsequently used for evaluation. Meanwhile, the enzyme classification (EC) number of the topranked reference reaction was considered as the EC number of the predicted biotransformation. Screening and expression of candidate enzymes. UniProt 14 was used as the source of candidate enzymes. Because the EC numbers of most enzymes in UniProt 14 are unknown, we annotated the EC number of the enzymes with Bio2Rxn 44 , a sequence-catalytic function model based on a consensus strategy. Subsequently, enzyme sequences belonging to the 3.1.1.-and 3.5.1.-categories were extracted, and the enzyme sequences and the molecular structure of ZEA and OTA were input into the PU-EPP model in pairs to obtain the predicted probability of whether a certain enzyme could catalyze a selected substrate. Then, according to the predicted probability, 10 enzymes for each mycotoxin with potential catalytic activity were selected for expression and activity verification (Table ). The gene sequences for coding candidate enzymes were synthesized and then cloned into the pD2P vector from Healthcode (). The constructed plasmids with candidate genes were expressed using the cell-free expression system provided by Healthcode according to their instructions. The control was a plasmid without candidate genes. After incubation at 30 °C for 4 h for targeted enzyme production, the supernatant of reaction mixtures containing enzymes was collected for subsequent mycotoxin degradation assays.