Drug-Drug interactions prediction calculations between cardiovascular drugs and antidepressants for discovering the potential co-medication risks

• DOI: 10.1371/journal.pone.0316021
• Publication Date: 2025-01-13T18:45:27Z
• Authors: Tie Hua Zhou; Tian Yu Jin; Xi Wei Wang; Ling Wang
• Keywords: Science; Q; R; Medicine; Research Article

Predicting Drug-Drug Interactions (DDIs) enables cost reduction and time savings in the drug discovery process, while effectively screening optimizing drugs. The intensification of societal aging increase life stress have led to a growing number patients suffering from both heart disease depression. These often need use cardiovascular drugs antidepressants for polypharmacy, but potential DDIs may compromise treatment effectiveness patient safety. To predict interactions between used treat these two diseases, we propose method named Multi-Drug Features Learning with Drug Relation Regularization (MDFLDRR). First, map feature vectors representing different spaces same. Second, relation regularization determine pair relationships interaction space. Experimental results demonstrate that MDFLDRR can be applied DDI prediction goals: predicting unobserved among within network inside outside network. Publicly available evidence confirms accurately identify antidepressants. Lastly, by utilizing structure calculations, ascertained severity newly discovered mine co-medication risks aid smart management pharmaceuticals.