A concise review on role of QSAR in drug design Prema V.1,*, Sivaramakrishnan Meera2, Rabiya M.2 1Department of Pharmaceutical Chemistry, K. K. College of Pharmacy, Gerugambakkam, Chennai-128, Tamil Nadu, India 2Department of Pharmacy Practice, K. K. College of Pharmacy, Gerugambakkam, Chennai – 128, Tamil Nadu, India *Corresponding Author E-mail: premapharmchemistry@gmail.com
Online Published on 13 February, 2024. Abstract QSAR, Quantitative structure-activity relationship has paved a way for itself into the practice of agrochemistry, pharmaceutical chemistry, toxicology and eventually most faces of chemistry for almost 40 years. Quantitative structure-activity relationships (QSAR) have been applied for decades in the establishment of relationships between physicochemical properties of chemical substances and their biological activities for making prediction regarding the activities of new chemical compounds using reliable statistical model. The fundamental principle underlying the decorum is that the difference in structural properties is responsible for the variations in biological activities of the compounds. However, this approach has only a limited utility for designing a new molecule due to the lack of consideration of the 3D structure of the molecules. Even though the trial-and-error factor which is involved in the development of a new drug cannot be ignored completely, QSAR possibly decreases the number of compounds to be synthesized by facilitating the selection of the most promising lead candidates. Many success stories of QSAR have attracted the medicinal chemists to investigate the relationships of structural properties with biological activity.1 Conscientious analysis and modification of independent variables has led to an expansion in development of molecular and atom-based descriptors, as well as descriptors derived from quantum chemical calculations and spectroscopy. The improvement in high-through-put screening procedures also contributes for rapid screening of large number of compounds under similar test conditions and thus minimizes the risk of combining variable test data from different sources. The overall goals of QSAR are to retain their original essence and remain focused on the predictive ability of the approach and its receptiveness to mechanistic interpretation. Top Keywords QSAR, 3D-QSAR, Physiochemical properties, Hansch analysis. Top |