Retrieval-Augmented Phytopharmacology: Connecting Literature Evidence, Molecular Databases, Pathway Knowledge, and Expert Review for Drug Discovery

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Phytopharmacology depends on heterogeneous and frequently fragmented evidence spanning botanical identity, phytochemical composition, chemical structures, bioactivity measurements, molecular targets, biological pathways, safety findings, and expert interpretation. Although retrieval-augmented approaches may improve access to this evidence, retrieval alone cannot establish molecular identity, biological mechanism, therapeutic relevance, safety, or candidate validity. This article proposes a conceptual retrieval-augmented phytopharmacology framework for evidence-grounded drug discovery reasoning. The framework begins with explicit research-question formulation and retrieves relevant peer-reviewed literature, natural-product and phytochemical records, molecular structures, bioactivity observations, target annotations, pathway knowledge, and ADMET or toxicity evidence. Retrieved information is then subjected to source grounding, citation traceability, database-provenance assessment, identity resolution, duplicate detection, conflict identification, and evidence-level classification. Pathway mappings and knowledge relationships are interpreted as contextual support for mechanistic hypotheses rather than proof of mechanism. Natural-product chemists, pharmacologists, toxicologists, and other relevant experts remain responsible for evaluating plausibility, resolving conflicts, identifying evidence gaps, and defining appropriate validation studies. Drug discovery decision logic is therefore limited to transparent hypothesis prioritisation, uncertainty communication, validation planning, and auditable research support. The principal contribution is an integrated framework that connects retrieval, molecular database integration, pathway interpretation, expert review, and decision boundaries while explicitly controlling hallucination, provenance loss, evidence overstatement, and unsupported clinical or candidate-level claims.