In-silico prediction of anti-breast cancer activity of ginger (Zingiber officinale) using machine learning techniques

Gondokesumo, Marisca Evalina and Rasyak, Muhammad Rezki (2024) In-silico prediction of anti-breast cancer activity of ginger (Zingiber officinale) using machine learning techniques. Breast Disease, 43 (1). pp. 99-110. ISSN 0888-6008; E-ISSN 1558-1551

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Molecular docking, machine learning, KIT, MAPK2
Subjects:	R Medicine > RS Pharmacy and materia medica
Divisions:	Faculty of Pharmacy > Department of Pharmacy
Depositing User:	Ester Sri W. 196039
Date Deposited:	16 May 2024 04:20
Last Modified:	22 May 2024 07:23
URI:	http://repository.ubaya.ac.id/id/eprint/46366

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