ArticleiScience2026
Mechanism-based prediction of drug synergy via network controllability analysis of therapeutic pathways in intractable diseases.
Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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7 authors.
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Abstract
Identifying effective drug combinations for multidrug therapies in intractable diseases remains challenging, and drug synergy depends on regulatory mechanisms. Herein, we introduce a mechanistic framework that redefines drug combination strategies into two distinct synergy paradigms-"boost" and "complement"-and present a network-based method called SYNERGIE to predict associated combinations across diseases. For diseases lacking known therapeutic targets, we introduced network controllability analysis to estimate therapeutic pathways and target molecules. SYNERGIE integrates drug-disease and drug-drug interactions across multiomics layers using Bayesian optimization to prioritize disease-state-specific combinations. SYNERGIE outperformed existing methods across 14 diseases, and identified both doublets and triplets. Therapeutic effects of triplets predicted for colorectal cancer were validated through
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