Anticancer activity prediction of Curcuma longa and Phyllanthus urinaria through computational analysis.

Abstract

Traditional Indonesian medicine has long been recognized for its curative qualities, although concerns remain over the efficacy and safety of medicinal herbs. The application of computational methods in novel drug discovery is one of the promising new insights offered by recent technical advancements. This study attempts to find putative anticancer chemicals in two extensively used plants in Southeast Asia, Curcuma longa and Phyllanthus urinaria, using a computational technique. AKT1, a model protein implicated in the development of cancer cells, was used in this investigation. In these two plants, 28 different chemicals were found. We use strict selection standards, like Lipinski's rule of five, to ensure the identification of potential candidates. The findings demonstrated that 24 compounds had comparable binding affinities to the reference ligands, indicating encouraging therapeutic potential. Subsequent investigation showed that the compounds' chemical structures differed and that their similarities to the reference ligand were <10%. However, for both plant-derived drugs, the amino acid binding patterns revealed remarkable similarities that went above 50% similarity, suggesting that both may be useful.