where 𝑋 is the number of corresponding substrates of an enzyme, 𝑋 %&' is the maximum of corresponding substrates of an enzyme, and 𝑋 %() is the minimum of corresponding substrates of an enzyme. In this way, we generated ~10 times as many "negative" enzyme-substrate pairs as known (positive) enzyme-substrate pairs. To save valuable data and computing resources, we adopted experience-based manual hyperparameter selection according to previously published studies 39 . Thus, we randomly selected 10,000 positive datapoints and 10,000 unlabeled datapoints as the test set and used the rest as the training set. The data in the test set were not "seen" by the models during the training process. Implementation of PU learning. The implementation of PU learning primarily consisted of three steps (Fig ). First, equal numbers of unlabeled and positive samples were sampled and input into the model. Second, the probability ranges of positive samples in each epoch were calculated, and were used for identifying and removing potential positive samples in the unlabeled dataset. Third, the potential positive samples were removed from the dataset while others were put back.