MFFL-DSR: A Multi-Feature Fusion Learning Method for Discovering the Synergistic Relationships between Psychotropic and Cardiovascular Drugs

Cardiovascular disease and depression often require combined use of cardiovascular and psychotropic drugs. This paper introduces Multi-Feature Fusion Learning for Discovering Synergistic Relationships (MFFL-DSR), a method to predict drug interactions. It first constructs matrices of drug features, including classification, targets, enzymes, pathways, and molecular structure. Drugs from different feature domains are then projected into a shared interaction domain. A regularization term is formulated to represent drug pairing relationships in the inter-action space, forming the MFFL-DSR objective function. Finally, iterative optimization identifies all potential drug combinations. The results show that MFFL-DSR outperforms baseline methods in six metrics: AUPR, AUC, Precision, Accuracy, Recall, and F1 score.