Artificial Intelligence-Driven Prioritization of Combination Therapies for Multigenic Diseases
Keywords:
artificial intelligence, combination therapy, multigenic disease, network medicine, computational prioritization, socio-technical infrastructureAbstract
Artificial intelligence is increasingly positioned as a decision-support layer for prioritizing combination therapies in multigenic diseases, where single-target interventions often fail. This paper examines the systems-level architecture required to translate molecular, pharmacological, and clinical data into actionable combination therapy rankings. The central argument is that the primary challenge is not only predictive accuracy but also the construction of robust socio-technical infrastructure capable of integrating heterogeneous evidence, network-level disease models, and adaptive learning loops. The discussion addresses structural trade-offs in centralized and federated architectures, data harmonization across omics and real-world health records, network-based reasoning, and deployment constraints in clinical environments. Special attention is given to fairness, transparency, regulatory alignment, and long-term sustainability. The prioritization of combination therapies requires balancing polypharmacology, patient heterogeneity, and uncertainty in mechanistic evidence. The paper explores how graph-based representations and community detection can reveal candidate synergies while cautioning against overreliance on black-box predictors. It concludes with policy recommendations for auditing, documentation, and continuous monitoring of AI-driven therapeutic prioritization systems.
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