Abstract
Natural products remain a major source of pharmacologically active scaffolds, but converting complex extracts into validated drug leads is still slow and uncertain. A major challenge is not metabolite detection itself, but establishing a reliable link between chemical identity, molecular target, mechanism of action, exposure, efficacy, and safety. LC–MS and GC–MS provide complementary metabolomic coverage, while spectral libraries, molecular networking, and dereplication strategies help prioritize compounds and avoid repeated isolation of known metabolites. Network pharmacology extends this analysis to compound–target–pathway relationships, whereas molecular docking and molecular dynamics (MD) provide structural hypotheses that can guide experimental testing. Artificial intelligence (AI) adds another layer of prioritization through spectral annotation, target and bioactivity prediction, ADMET assessment, and lead optimization. Yet greater computational complexity does not necessarily produce stronger biological evidence. Annotation errors, database bias, uncertain target assignments, inadequate molecular sampling, and poorly defined applicability domains can propagate across an integrated workflow and produce apparently coherent but weakly supported conclusions. This review examines how metabolomics, network pharmacology, molecular simulation, and AI can be combined without conflating prediction with validation. Particular emphasis is placed on confidence-ranked metabolite identification, reproducibility, data provenance, uncertainty, and the experimental evidence required to support mechanistic claims. We propose an evidence-gated workflow in which computational methods are used primarily to rank hypotheses and direct experiments, while biochemical validation, cellular target engagement, pharmacokinetics, in vivo efficacy, and toxicological assessment progressively establish biological confidence. Such integration can shorten the path from metabolomic fingerprints to therapeutic leads, provided that analytical confidence and experimental validation remain the basis for decision-making. (Open J Biomed Res 2026;5:88-101)