Machine Learning in Natural Product Separation and Purification Across Chromatographic Method Development, Peak Resolution, Fraction Prioritization, and Process Optimization

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Natural-product discovery fundamentally depends on the ability to separate chemically heterogeneous mixtures before assigning structures, activities, or mechanisms to individual constituents. Machine learning is increasingly applied to chromatographic method development, retention prediction, signal processing, fraction prioritization, and experimental optimization, yet these applications are often discussed as if they represented a single analytical problem. This methodological review distinguishes four different computational roles across the separation-to-purification continuum: predicting chromatographic behavior, interpreting chromatographic signals, prioritizing chemically informative or bioactive fractions, and controlling experimental search. The evidence indicates that retention and selectivity models are becoming more chemically and method aware, while peak-processing models can automate difficult signal-recognition tasks, and adaptive optimization can reduce unguided exploration of chromatographic conditions. Natural-product-specific studies further show that metabolomic features can support fraction prioritization before complete structural characterization. However, analytical prediction, signal resolution, fraction ranking, and preparative purification are not interchangeable validation levels. Generalization remains constrained by chemical-space shift, incomplete chromatographic metadata, instrument and column variation, preprocessing choices, objective specification, experimental budget, and sparse direct evidence connecting analytical machine-learning performance with preparative natural-product yield and purity. We therefore propose a layered methodological framework in which machine learning is evaluated according to the chromatographic object it learns, the decision it informs, and the experimental boundary across which it is expected to transfer, thereby supporting more rigorous comparison of methods while preserving the role of expert chromatography in defining objectives, diagnosing failure, and validating purification outcomes.