Graphormer-Integrated Interaction Network Construction and Action Prediction of Major Bupleurum Chinense Compounds and Targets

Abstract

To address the low accuracy of multi-component-multi-target interaction prediction in Bupleurum chinense caused by complex molecular structures and limited target associations, this paper employs advanced graph models combined with biological network topology information. The same graph-based representation strategy is relevant to materials informatics, including the screening of molecular or polymer structures for electromagnetic sensing, dielectric response, and functional coating design. Specifically, RDKit is used to construct chemically defined molecular graphs, with atoms as nodes and covalent bonds as edges, and to encode multidimensional atomic features. These graphs are directly input into Graphormer. Graphormer then incorporates a fully connected attention mechanism that integrates topological distances and spatial geometric information from RDKit graphs to extract higher-order molecular representations. Node2Vec is used to embed the protein-protein interaction network in a low-dimensional space and identify potential target pathways. Finally, compound and protein embeddings are concatenated and fed into a two-channel neural network to predict interaction probabilities. With negative sampling for training, the method achieves stable average AUROC values of 0.91-0.93 and AUPRC values of 0.88-0.90. Even when only 10% of targets are visible, the F1-score reaches 0.683±0.021 and Coverage Rate@50 reaches 76.4%, indicating strong inductive reasoning under sparse knowledge conditions.