In Silico Prediction, Computational Physico Chemical Analysis in Gymnemic Acids

Life Sciences-In Silico study for better drug discovery

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.