%0 Journal Article
%T AI-Assisted Structure Elucidation of Natural Products: A Methodological Review of Spectral Prediction, Candidate Generation, Confidence Calibration, and Human Verification
%A Ethan Wright
%A Chloe Bennett
%A Jack Turner
%A Olivia Harris
%J Specialty Journal of Pharmacognosy, Phytochemistry, and Biotechnology
%@ 3062-441X
%D 2026
%V 6
%N 1
%R 10.51847/113Qone9P1
%P 153-163
%X Artificial intelligence is increasingly embedded in natural-product structure elucidation, but the term encompasses methodologically distinct operations whose outputs carry different evidential meanings. This methodological review examines where AI enters the elucidation workflow, how spectral prediction and inverse inference differ, how candidates are generated and ranked, how complementary analytical modalities can be integrated, and why confidence and human verification remain separate problems from predictive accuracy. Recent methods span learned nuclear magnetic resonance representations, forward chemical-shift and tandem-mass-spectrum prediction, formula and fingerprint inference, database-assisted ranking, de novo molecular generation, multimodal spectrum-to-structure systems, and hybrid physics-guided approaches. Their capabilities are substantial but bounded by training chemistry, acquisition conditions, reference-database composition, candidate-space definition, and the distinction between simulated and experimental spectra. The central synthesis proposed here is an evidence-qualified architecture in which AI outputs progress through separable states: spectral representation or prediction, candidate construction, cross-modal reconciliation, domain-qualified confidence, and chemical verification. These states should not be collapsed into a single claim of “structure elucidation.” Particular caution is required for structurally novel natural products, stereochemically dense candidates, sparse reference regions, and model outputs obtained outside the calibration domain. The review argues that methodological progress should therefore be judged not only by benchmark accuracy but also by transferability, uncertainty calibration, candidate traceability, contradiction handling, and the capacity to support or trigger expert reassessment. AI can materially reorganize structure-elucidation practice, but defensible assignment still depends on matching computational claims to the strength and scope of analytical evidence.
%U https://galaxypub.co/article/ai-assisted-structure-elucidation-of-natural-products-a-methodological-review-of-spectral-predictio-t0v6xtyrzuytmar