{Reference Type}: Journal Article
{Title}: In-silico prediction of anti-breast cancer activity of ginger (Zingiber officinale) using machine learning techniques.
{Author}: Gondokesumo ME;Rasyak MR;
{Journal}: Breast Dis
{Volume}: 43
{Issue}: 1
{Year}: 2024
暂无{DOI}: 10.3233/BD-249002
{Abstract}: <B>UNASSIGNED: </B>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.<BR><B>UNASSIGNED: </B>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.<BR><B>UNASSIGNED: </B>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.<BR><B>UNASSIGNED: </B>Overall, this study shows the value of machine learning in drug development and offers insightful information on possible anticancer chemicals from ginger.