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0This review examines the shift from traditional single-target therapies toward rational polypharmacology through the integration of artificial intelligence and network biology. By conceptualizing diseases as failures within complex signaling webs rather than isolated malfunctions, researchers use machine learning and graph neural networks to identify multi-target intervention sites that circumvent biological redundancy and drug resistance. The text details how generative models and reinforcement learning can design novel compounds capable of modulating entire pathways, such as those involved in oncology and neurodegeneration. To bridge the gap between computational prediction and clinical use, the authors emphasize a closed-loop discovery cycle involving experimental validation and multi-omics integration. Ultimately, the sources advocate for an AI-driven, systems-level approach to pharmaceutical design that addresses the intricate cross-talk and feedback loops inherent in human biology.
References:
Li X, Wu Z, Fang T, et al. AI and network biology for rational polypharmacology in signaling drug design: a review[J]. npj Precision Oncology, 2026.
