Ginseng has gained its popularity as an adaptogen since ancient days because of its triterpenoid saponins, known as ginsenosides. These triterpenoid saponins are unique and classified as protopanaxatriol and protopanaxadiol saponins based on their glycosylation patterns. They play many protective roles in humans and are under intense research as various groups continue to study their efficacy at the molecular level in various disorders. Ginsenosides Rb1 and Rg1 are the most abundant ginsenosides present in ginseng roots, and they confer the pharmacological properties of the plant, whereas ginsenoside Rg3 is abundantly present in Korean Red Ginseng preparation, which is highly known for its anticancer effects. These ginsenosides have a unique mode of action in modulating various signaling cascades and networks in different tissues. Their effect depends on the bioavailability and the physiological status of the cell. Mostly they amplify the response by stimulating phosphotidylinositol-4,5-bisphosphate 3-kinase/protein kinase B pathway, caspase-3/caspase-9-mediated apoptotic pathway, adenosine monophosphate-activated protein kinase, and nuclear factor kappa-light-chain-enhancer of activated B cells signaling. Furthermore, they trigger receptors such as estrogen receptor, glucocorticoid receptor, and N-methyl-d-aspartate receptor. This review critically evaluates the signaling pathways attenuated by ginsenosides Rb1, Rg1, and Rg3 in various tissues with emphasis on cancer, diabetes, cardiovascular diseases, and neurodegenerative disorders.

Anticancer peptides (ACPs) are promising therapeutic agents for targeting and killing cancer cells. The accurate prediction of ACPs from given peptide sequences remains as an open problem in the field of immunoinformatics. Recently, machine learning algorithms have emerged as a promising tool for helping experimental scientists predict ACPs. However, the performance of existing methods still needs to be improved. In this study, we present a novel approach for the accurate prediction of ACPs, which involves the following two steps: (i) We applied a two-step feature selection protocol on seven feature encodings that cover various aspects of sequence information (composition-based, physicochemical properties and profiles) and obtained their corresponding optimal feature-based models. The resultant predicted probabilities of ACPs were further utilized as feature vectors. (ii) The predicted probability feature vectors were in turn used as an input to support vector machine to develop the final prediction model called mACPpred. Cross-validation analysis showed that the proposed predictor performs significantly better than individual feature encodings. Furthermore, mACPpred significantly outperformed the existing methods compared in this study when objectively evaluated on an independent dataset.