Fig. 1

The MuFRF workflow. Feature Extraction and Representation (left part): MuFRF feeds the 2-D molecular graph converted by 1-D SMILES into GIN consisting of message-passing layer and readout layer to learn the graph-view feature representation \(h_{G}\). Meanwhile, MuFRF employs the RotatE to obtain the KG-view feature representation \(e_{h}\) of entities in KG. Latent Feature Fusion (middle part): we employ concatenate-level and scalar-level strategies to fuse structure information with semantic features in KG, CNNs and auto-encoder further excavate more effective features, and a Multi-head attention module achieves the final latent feature fusion. Classification (right part): the fully connected layer receives the concatenation of latent feature representation and initial graph-based structure representation and KG-based representation to predict potential DDIs