Deep learning relies on large-scale data to make high-quality predictions. To afford a comprehensive dataset on enzyme promiscuity, we integrated data from the Rhea 22 , Kyoto Encyclopedia of Genes and Genomes (KEGG) 23 , MetaCyc 24 , Brenda 25 , and RxnFinder 26,27 databases and acquired 170,179 enzymes, 5,837 substrates, and 606,555 corresponding enzymesubstrate pairs. Since negative data are rarely reported, previous studies usually augmented negative samples for training classification models based on activity thresholds or random sampling 28,29 . However, low activity is not equivalent to inactivity, and certain randomly sampled enzymesubstrate pairs may actually be functional (positive) because of enzyme promiscuity, which would result in the deep learning models acquiring inaccurate results. Therefore, we proposed a strategy that combines weighted random sampling and positive unlabeled (PU) learning to minimize the impact of inaccurate negative samples (see the methods section for detail). The weighted random sampling strategy assumes that enzymes with fewer reported substrates have higher catalytic specificity. Therefore, to reflect both enzyme promiscuity and specificity, more corresponding negative enzyme-substrate pairs should be included in the training set. Accordingly, we generated 6,488,914 "negative" samples with a ratio of ~10 times the positive samples (Fig 2A and). However, since some enzymes may be promiscuous, some positive samples might remain in the generated negative samples. If these cases are ignored, the model would ignore the enzyme's promiscuity. Therefore, to ensure that the model accurately predicts enzyme behavior, we considered these generated samples as unlabeled data and proposed a PU learning-based enzyme promiscuity prediction (PU-EPP) model. This model employs a PU learning mechanism during training to exclude potential positive samples in the unlabeled data according to the probability ranges of positive samples (Fig 2B ) 30 . We chose the 90 th percentile of the probability distribution of positive samples as the threshold because it presents both good performance and robust recognition ability of potential positive samples . The PU-EPP model was built based on a multi-head attention mechanism (Fig ) 31 . The substrate features were extracted through graph neural networks, while the protein sequence was encoded using a Continuous Bagof-Words 32-34 . The enzyme sequence was encoded with the Continuous Bag-of-Words model, while substrate features were extracted from the molecular structure of substrates with graph neural networks. Extracted enzyme features were input into the encoder. Extracted substrate features were processed through a self-attention layer and then combined with enzyme features. Then, the combined features were input into another self-attention layer (interaction layer) to extract interaction information between the substrates and the enzymes. Finally, the features were input into a fully connected layer and a layer normalization step to achieve the final output by softmax. FC: fully connected layer; Add: residual connection; Norm: layer normalization.