Abstract: Analyzing the molecular mechanisms of classic traditional Chinese medicine (TCM) formulas is essential for interpreting TCM principles through modern scientific frameworks, and serves as a cornerstone for establishing dominant disease categories of TCM. However, the lack of correlation with disease progression characteristics and clinical efficacy has become a scientific issue in this field that requires urgent resolution. To address this issue, this paper employs the classic formulas "Yuquan Pill" and "Xiaoke Prescription" for the treatment of "Xiaoke", a dominant disease in TCM, as a demonstration. Adhering to an "originating from clinic-returning to clinic" strategy, omics integrated with generative artificial intelligence is employed to identify active molecules and their biological associations in the formula, thereby uncovering integrated feature PANEL of disease progression and outcomes. This approach bridges the "clinical-basic" gap, enabling the characterization of emergent pharmacodynamic features that arise from metabolic network oscillations and chaotic dynamics. It reveals the synergistic principles of active molecules and their biological mechanisms in classic formulas. Returning to clinical practice, the focus is on validating the active molecules and discovering novel multi-component formulas with superior efficacy to that of classical formulas. This approach establishes a new paradigm for clearly elucidating and comprehensively clarifying the complex active molecules in classic TCM formulas.