Pharmaco-Informatics for Natural Product Repurposing: Disease Similarity, Target Networks, Molecular Signatures, and Therapeutic Hypothesis Generation

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Natural product repurposing has become an important topic in computational pharmacology because plant-derived compounds, phytochemicals, and natural product candidates often interact with multiple targets, pathways, and biological contexts. However, repurposing logic can be overstated when computational associations are interpreted as evidence of efficacy, safety, or clinical readiness. This article proposes an original pharmaco-informatics framework for natural product repurposing that treats disease similarity, target networks, molecular signatures, pathway relationships, compound–target evidence, safety signals, exposure plausibility, and evidence provenance as components of therapeutic hypothesis generation. The framework positions disease ontology and phenotype similarity as entry points for defining disease relevance, target-network mapping as a means of assessing mechanistic plausibility, and molecular-signature analysis as a perturbational layer for identifying alignment or reversal hypotheses. Candidate ranking is framed as evidence integration rather than therapeutic prioritisation, requiring explicit attention to compound identity, target confidence, pathway coherence, omics context, ADMET and toxicity evidence, herb–drug interaction risk, uncertainty grading, and validation readiness. The main contribution is a transparent decision-support logic that connects natural product informatics with repurposing hypothesis generation while preserving interpretation boundaries. The framework is intended to guide research prioritisation, experimental planning, and cautious translation rather than to establish therapeutic efficacy, clinical usefulness, or regulatory acceptability.