Natural-product structure prioritization increasingly uses machine-learning models that map tandem mass spectra, nuclear magnetic resonance data, or combinations of spectral modalities to candidate structures. Yet a high model score does not establish that a query lies within the chemistry, acquisition conditions, spectral information regime, or candidate space represented during model development. This distinction matters especially for natural products, where scaffold novelty, sparse reference coverage, heterogeneous acquisition, and incomplete spectral evidence can coincide. This article develops an original computational architecture for uncertainty-calibrated structure prioritization. The analysis separates chemical familiarity from predictive uncertainty, defines spectral-model domain as multidimensional rather than purely similarity-based, decomposes uncertainty across measurement, formula inference, spectral prediction, learned representation, candidate generation, ranking, and calibration, and proposes explicit decision states for ranking, qualified acceptance, abstention, and human review. MS/MS and NMR are treated as complementary but non-interchangeable evidence sources whose uncertainties should remain visible before fusion. The architecture further proposes that domain status, candidate-set completeness, and calibration be evaluated separately and that benchmark design deliberately challenge models with unfamiliar chemistry and shifted analytical conditions rather than relying only on random held-out tests. The framework is conceptual rather than prospectively validated; its thresholds, fusion rules, abstention criteria, and transferability across instruments, chemical classes, and laboratories require empirical testing. Its intended contribution is therefore not a new performance claim, but a reliability-centered design logic for deciding when spectral predictions should be trusted, qualified, expanded, or withheld.
