INTRODUCTION: Indonesian civilization extensively uses traditional medicine to cure illnesses and preserve health. The lack of
knowledge on the security and efficacy of medicinal plants is still a significant concern. Although the precise chemicals responsible
for this impact are unknown, ginger is a common medicinal plant in Southeast Asia that may have anticancer qualities.
METHOD: Using data from Dudedocking, a machine-learning model was created to predict possible breast anticancer chemicals
from ginger. The model was used to forecast substances that block KIT and MAPK2 proteins, essential elements in breast
cancer.
RESULT: Beta-carotene, 5-Hydroxy-74′ -dimethoxyflavone, [12]-Shogaol, Isogingerenone B, curcumin, Trans-[10]-Shogaol,
Gingerenone A, Dihydrocurcumin, and demethoxycurcumin were all superior to the reference ligand for MAPK2, according
to molecular docking studies. Lycopene, [8]-Shogaol, [6]-Shogaol, and [1]-Paradol exhibited low toxicity and no Lipinski
violations, but beta carotene had toxic predictions and Lipinski violations. It was anticipated that all three substances would
have anticarcinogenic qualities.
CONCLUSION: Overall, this study shows the value of machine learning in drug development and offers insightful information
on possible anticancer chemicals from ginger