Computational frameworks transform antagonism to synergy in optimizing combination therapies.
review · Level V
Where this comes from
- Record sourced from PubMed, PMID 39828791.
- Also identified by DOI 10.1038/s41746-025-01435-2 and PMC identifier 11743742.
- Licence recorded as CC BY-NC-ND.
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Abstract
While drug combinations are increasingly important in disease treatment, predicting their therapeutic interactions remains challenging. This review systematically analyzes computational methods for predicting drug combination effects through multi-omics data integration. We comprehensively assess key algorithms including DrugComboRanker and AuDNNsynergy, and evaluate integration approaches encompassing kernel regression and graph networks. The review elucidates artificial intelligence applications in predicting drug synergistic and antagonistic effects.