Natural products are frequently described as polypharmacological because multiple proteins, pathways, or phenotypes become associated with a single compound. Yet target multiplicity can arise from fundamentally different evidentiary processes, including database aggregation, computational prediction, assay interference, nonspecific activity, proteomic target engagement, or functionally consequential interactions. Treating these states as mechanistically equivalent risks converting annotation density into apparent biological complexity. This article develops an original mechanistic evidence model for evaluating multi-target natural-product claims. The analysis distinguishes target nomination from experimental engagement and separates experimentally observed promiscuity from functional multi-target action. The proposed model treats confidence as an evidence-escalation problem rather than a target-count problem: stronger mechanistic interpretation requires progressively greater control of data provenance, assay artifacts, physical target engagement, orthogonal confirmation, functional perturbation, and biological context. Importantly, confidence is assigned separately to individual target claims and to the higher-order claim that several targets jointly contribute to a phenotype. This prevents one well-supported target from legitimizing weak secondary annotations while preserving genuine polypharmacology when multiple interactions withstand mechanistic testing. The framework also accommodates difficult cases, including concentration-dependent target switching, covalent reactivity, indirect proteomic responses, metabolite-mediated activity, and one dominant target producing distributed downstream effects. The model is proposed as an evidence-organizing architecture rather than a prospectively validated scoring system. Its value therefore lies in clarifying what available evidence can establish, what remains inferential, and what additional experiments are required before multi-target natural-product mechanisms should be interpreted as functionally real.
