Artificial Intelligence‐Driven Network Pharmacology: A Methodological Paradigm Shift Bridging Traditional Wisdom and Modern Science

Traditional network pharmacology (NP) is fundamentally constrained by its reliance on static, two‐dimensional topological mapping and pervasive data sparsity, limiting its capacity to capture the dynamic complexity of biological systems. To transcend these descriptive boundaries, we propose and operationalize AI‐NP as a rigorous, multimodal computational framework that reconceptualizes biological networks as dynamic, learnable manifolds rather than fixed graphs. The paradigm is organized around four core methodological pillars: knowledge graph engineering, geometric graph learning, multimodal state anchoring, and generative reasoning paradigms. By integrating these pillars, AI‐NP enables the mechanistic decoding of complex polypharmacology inherent in multicomponent herbal formulas. Leveraging state‐of‐the‐art architectures, including graph attention‐aware fusion networks for zero‐shot link prediction and SO(3)‐equivariant message‐passing models for 3D stereochemical resolution, AI‐NP establishes a computationally rigorous pipeline that transitions the field from empirical observation to mechanistic prediction. This paradigm shift offers a scalable and scientifically validated pathway for discovering effective, equitable therapeutics from traditional medicine systems.