Can Machine Learning Resolve Ambiguous Natural Product Structures Using Spectroscopic Evidence, Molecular Constraints, Candidate Ranking, and Automated Structure Elucidation?

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Natural-product structure elucidation remains a challenging inference problem because spectroscopic evidence is often incomplete, noisy, or nonunique, requiring reconciliation with molecular formulae, chemical plausibility, and stereochemical alternatives. Machine learning has increasingly been applied across this workflow, yet the term "identification" encompasses heterogeneous tasks from peak interpretation and formula prediction to full-structure generation. This scoping review mapped peer-reviewed evidence published from 2017 to 2025 concerning machine-learning-assisted structure inference for natural products and small molecules using NMR, tandem mass spectrometry, infrared, UV, and multimodal spectral data. Following a PRISMA-ScR-compatible evidence-mapping approach, we charted analytical modalities, prediction tasks, molecular constraints, candidate-generation strategies, ranking methods, validation designs, uncertainty treatments, and transferability limits. The mapped literature reveals a distributed automation landscape rather than a single solved workflow. NMR-based methods increasingly extract structure-sensitive constraints, mass-spectral approaches narrow formula and candidate spaces while inferring classes and fingerprints, and infrared or multimodal models contribute orthogonal evidence. Probabilistic and learned scoring systems prioritize competing candidates effectively. However, exact two-dimensional structure recovery, stereochemical assignment, confidence calibration, candidate-set completeness, and out-of-domain transfer remain distinct unresolved challenges. We therefore propose a constrained-inference interpretation in which machine learning progressively reduces ambiguity through staged constraint extraction and candidate prioritization, rather than replacing structural proof with a single model score. Current evidence supports machine learning as a powerful assistive tool, but fully autonomous natural-product identification is not yet universally achievable.