Tracing Mechanistic Hypotheses from Networks to Experiments in Dieda Qili Tablet Research

Abstract

Network pharmacology can organize possible relationships among a multicomponent formulation, molecular targets, and a disease process. It cannot by itself identify which relationship explains a treatment effect. This article uses research on Dieda Qili Tablet and fracture healing to distinguish candidate generation, expression discrimination, molecular compatibility, and causal mechanism. The motivating study combines network analysis, expression-based machine learning, docking, and molecular simulation. Those components answer different questions and can share prior information that makes apparent convergence less independent than it appears. We develop a claim-by-claim evidence framework covering target provenance, network degree, expression contrasts, nested model evaluation, enrichment backgrounds, and physical target engagement. Efficient language-model adaptation may assist evidence extraction, but its output remains another measurement requiring verification. The article presents a methodological synthesis and a prospective validation design, not a new analysis of patient expression data or a demonstration of clinical efficacy. Keywords: network pharmacology; fracture healing; target prioritization; expression classification; molecular docking; causal interpretation.