Fig. 1

The workflows of the MFSynDCP model framework process. Drug molecular graphs are generated based on the SMILES sequences of drugs, and their feature embeddings are obtained using a GAT. Additionally, feature embeddings of cancer cell line gene expression profiles are acquired using a MLP. The embedding vectors of the drugs and cell lines are then concatenated and input into a multi-source feature interactive learning controller for the fusion of multi-source features. Finally, the fused features are fed into the prediction module for predicting the synergistic effects of drug combinations