Abstract

Helit Jain60613*, Dhananjay Meshram60614 and Sharav Desai60615

During the past 40 years, innovations in drug design and quantitative structure activity relationship have been applied to agrochemistry, pharmaceutical chemistry, toxicology, and eventually most aspects of chemistry. A Quantitative Structure Activity Relationship (QSAR) has been used for the past few decades to develop a reliable statistical model for predicting the advance activities of newly and existing chemicals by incubating their relationship and physicochemical properties. As an academic tool, QSAR is used to allow for rational prediction of biological activity and physicochemical properties and intensifying and rationalizing the mechanism of action of the series of chemicals. The use of QSAR includes mathematical methods and machine learning approaches like Support Vector Machines, Linear Regression, Partial Least Squares, and Neural Networks. Review includes the application of QSAR on drug discovery, high throughput screening, anti-HIV activity and identification of viral 3CLpro and RdRp compounds in COVID-19.