Traditional Knowledge Meets Molecular Networks: A Systems-Level Re-evaluation of Medicinal Plant Bioactivity

Abstract

Traditional medicinal knowledge has played a significant role in the identification of bioactive plants through the many times it has led to the identification of those plants, however, there have been many times when traditional evaluation turned into isolated compounds and single targets. The review revisits the bioactivity of medicinal plants by systematically linking ethnobotanic knowledge, phytochemical diversity, molecular networks, multi-omics information, computational prediction and experimental validation. The relationship among constituents, targets, pathways, diseases, and phenotypes can be mapped using network pharmacology; the high throughput of metabolomics, transcriptomics, and proteomics, molecular docking, and machine learning can provide mechanistic plausible leads for the prioritisation. But network diagrams are models, not realities; authenticated plant material, quantitative chemical standardisation, exposure responsive target assessment, orthogonal assays, causal perturbation, reproducibility, pharmacokinetics and clinically relevant outcomes are all necessary to achieve reliable translation.
Whole-extract effects cannot be inferred from individual compounds, as effects may be additive, synergistic, antagonistic or via bioavailability modification. Technical innovation should be accompanied by ethical governance, protection of indigenous knowledge, benefit sharing, biodiversity conservation and quality standards for regulations. The computational-experimental cycle can largely retain the context of traditional knowledge whilst transforming complex botanical preparations into clinical, reproducible and testable therapeutic hypotheses.