# Unifying Phytochemistry, Analytics, and Target Prediction to Advance Dendropanax morbifera Bioactive Discovery

## TL;DR

This review proposes a unified approach to study the medicinal plant Dendropanax morbifera by combining chemical analysis and computational methods to better understand its bioactive compounds.

## Contribution

The paper introduces an integrated framework for standardized analysis and target prediction to advance the development of Dendropanax morbifera-derived materials.

## Key findings

- DM contains diverse compounds like phenolic acids, flavonoids, and triterpenoids with varying distributions in plant parts.
- Standardized extraction and HPLC methods are needed to improve analytical reproducibility and translational application.
- In silico target prediction can help prioritize molecular targets for experimental validation.

## Abstract

Dendropanax morbifera (DM; “Hwangchil”) is an evergreen tree native to southern Korea and Jeju Island, traditionally used for detoxification, anti-inflammatory, immunomodulatory, and neuroprotective purposes. Recent studies indicate that DM extracts and their constituents exhibit a broad range of biological activities, including antioxidant, anti-inflammatory, antimicrobial, anticancer, antidiabetic, hepatoprotective, and neuroprotective effects. Phytochemical investigations have revealed a chemically diverse profile comprising phenolic acids, flavonoids, diterpenoids, triterpenoids—most notably dendropanoxide—and polyacetylenes, with marked variation in compound distribution across plant parts. Despite this progress, translational application remains constrained by the lack of standardized extraction protocols, substantial variability in high-performance liquid chromatography (HPLC) methodologies, and limited mechanistic validation of reported bioactivities. This review proposes an integrated framework that links extraction strategies tailored to compound class and plant part with standardized C18 reverse-phase HPLC conditions to enhance analytical reproducibility. In parallel, in silico target prediction using SwissTargetPrediction is applied as a hypothesis-generating approach to prioritize potential molecular targets for subsequent experimental validation. By emphasizing methodological harmonization, critical evaluation of evidence levels, and systems-level consideration of multi-compound interactions, this review aims to clarify structure–activity relationships, support pharmacokinetic and safety assessment, and facilitate the rational development of DM-derived materials for medical, nutritional, and cosmetic applications.