Target rule exploration of drug combination based on directed weighted network

Abstract Background In the realm of drug discovery, deciphering the interaction rules of drug combinations at the target level within biological networks is pivotal for developing effective therapeutic strategies. This study introduces a novel method for identifying drug combinations using a directed weighted network model. This model is developed by analyzing drug-directed information, target-directed information, and potential dynamic global changes in drug action within the network. Results By leveraging network topology relationships, the target regularity of drug combinations is investigated, and a corresponding discriminant algorithm is designed. Comparative analysis with existing models demonstrates the superior prediction accuracy of our approach. The results highlight the efficacy of our method in identifying various types of drug combinations, bridging the gap between current research on biological network-based drug combinations and actual drug action information. Furthermore, our approach reveals potential synergistic or antagonistic mechanisms underlying these combinations, providing valuable insights for the development of combination therapies. Conclusions Our findings confirm that the proposed method effectively identifies different types of drug combinations and provides a deeper understanding of the mechanisms behind these combinations. The study offers a robust tool for the rational design of drug combinations, potentially enhancing therapeutic efficacy and reducing adverse effects.