# From Tea to Topical Agent: Machine Learning and Bioinformatics Reveal KU DING Tea’s Anti-UV Ingredients and Mechanisms

## TL;DR

This study uses machine learning and bioinformatics to identify anti-UV ingredients in KU DING tea and explain how they might work.

## Contribution

The novel use of machine learning and network pharmacology to explore KU DING tea's anti-UV mechanisms and transdermal potential.

## Key findings

- 76 chemical components were identified in KU DING tea, with 21 predicted to have good transdermal potential.
- Machine learning predicted salicylic acid and methyl salicylate as likely anti-UV components.
- Network pharmacology suggested MAPK14 and NFKB1 as key targets in the AGE-RAGE signaling pathway.

## Abstract

Objectives: KU DING tea is a traditional Chinese herbal tea traditionally used topically for inflammation. This study aimed to investigate its potential anti-UV effects. Methods: The chemical components of KU DING tea were identified using UHPLC-Q-TOF-MS. Permeability prediction was performed to assess transdermal potential. A machine learning was applied to predict anti-UV activity, and network pharmacology analysis was used to explore the potential mechanism of action. Result: A total of 76 chemical components were identified, with 21 predicted to have good transdermal potential. A machine learning Random Forest (RF) model (accuracy 0.84, F1 0.84, AUC 0.93) predicted components like salicylic acid and methyl salicylate likely possess significant anti-UV activity. Network pharmacology indicated the mechanism may involve targets MAPK14 and NFKB1, influencing the AGE-RAGE signaling pathway. Conclusions: KU DING tea is a promising natural and safe anti-UV agent, deserving further experimental validation.