From implicit prioritization to auditable decisions in natural product drug discovery

Natural-product discovery increasingly benefits from metabolomics, high-resolution mass spectrometry, molecular networking, cheminformatics and artificial intelligence; but analytical capacity does not ensure explicit, reproducible decisions. We propose a framework for prospectively formalizing how evidence is translated into auditable experimental actions. Three recurring gaps are addressed: incomplete integration of sample metadata, conflation of analytical detection with molecular novelty and lack of explicit decision thresholds. A four-tier architecture links source authentication, reproducible chemical fingerprinting, orthogonal prioritization and definitive characterization through documented decision gates. An 'evidence ladder' separates molecular-identification confidence from chemical novelty, biological novelty and translational relevance. The framework is intended to make an existing, largely tacit decision process more transparent, comparable and testable across laboratories.