To ensure that the model had acceptable prediction ability, the PU mechanism was conducted after the model achieved an AUC >0.80. This process was repeated until the end of the training. Furthermore, the 90 th percentile of the probability distribution of positive samples was chosen as the threshold for the exclusion of potential positive samples in the unlabeled dataset . Construction of the deep learning pipeline. Graph neural networks (GNNs) were used to extract substrate features 40 . Substrates were considered as molecular graphs G = (𝜈, 𝜖). Each atom 𝑣 ( ∈ 𝜈 was represented by a 46-dimensional vector, which was the concatenation of one-hot encodings representing the atom types, degrees of the atom, chirality, hybridization types, number of radical electrons, number of hydrogen atoms attached, explicit valence, implicit valence, and aromaticity of the corresponding atoms. 𝜖 was the set of covalent bonds 6𝑣 ( , 𝑣 * 7 ∈ 𝜖, in a molecule represented as an adjacency matrix 𝐴 ∈ 𝑅 +×+ , in which N was the number of atoms in the molecule. We used the following layer-wise propagation rules to extract substrate features: