AI-Enabled Neuroprotective Natural Products: Network Biology, Blood–Brain Barrier Prediction, Target Selectivity, and Safety-Aware Discovery Logic

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Natural products provide structurally diverse starting points for neuropharmacology research, but computational evidence of target association or pathway relevance is insufficient to establish neuroprotection, central nervous system exposure, safety, or therapeutic utility. This original computational framework article proposes a structured discovery logic for artificial intelligence-enabled prioritization of natural products and natural product-inspired candidates with potential neuroprotective relevance. The framework begins with verified botanical, phytochemical, structural, stereochemical, bioactivity, assay, and provenance information and then connects neuroprotective hypotheses to disease or phenotype context. Network biology is used to organize compound–target–pathway relationships, while target-confidence, target-selectivity, target-engagement, and off-target analyses constrain mechanistic interpretation. Blood–brain barrier prediction is treated as one component of a wider CNS-relevance assessment that also considers passive permeability, transporter and efflux behavior, metabolic stability, ADME evidence, applicability domain, out-of-distribution status, and predictive uncertainty. Safety-aware discovery incorporates neurotoxicity, systemic toxicity, safety pharmacology, metabolism-related concerns, and herb–drug or drug–drug interaction risk. Advancement occurs only through predefined validation gates and expert neuropharmacology and toxicology review. The principal contribution is an evidence-linked, uncertainty-aware, and claim-bounded framework that supports research prioritization while explicitly preventing computational predictions from being presented as validated neuroprotection, demonstrated brain exposure, established safety, clinical utility, or treatment guidance.