In Silico Prediction, Computational Physico Chemical Analysis in Gymnemic Acids

Abstract

Gymnema sylvestre (Asclepiadaceae) also known as ‘gurmar’ or ‘sugar destroyer’ is a woody, climbing traditional medicinal herb which has many therapeutic applications in the Ayurvedic system of medicine. We present an overview of the most important databases with 2 gymnemic acid structural information about drugs and drug candidates, and of databases with relevant properties. Access to experimental data and numerical methods for selecting and utilizing these data is crucial for developing accurate predictive in silico models. Many interesting predictive methods for classifying the suitability of chemical compounds as potential drugs, as well as for predicting their physico-chemical and ADMET properties have been proposed in recent years. The gymnemic acids act as therapeutic agents and play vital roles in many therapeutic applications. Gymnemic acids are thought to be responsible for its anti-diabetic activity and are the major component of an extract shown to stimulate insulin release. It is also screened for bioavailability study, physicochemical study, drug likeness study, medicinal chemical analysis and target prediction. These methods are discussed, and some possible future directions in this rapidly developing field are also described. The commercial exploitation of this plant and its secondary metabolites are some of the major perspectives of this rare medicinal herb. The focus of the present study is to achieve the potential of therapeutic value of this herb its mechanism,and the action of their secondary metabolites.