About Authors:
Rakesh Bhatia*
School of Pharmaceutical Sciences,
Department of Pharmaceutical Chemistry,
Jaipur National University,
Jaipur-302025 (Rajasthan), India
*rakesh.mpharm1304@yahoo.com
Abstract
Chemical synthesis data and their biological screening have provided a vast amount of experimental data. As a result of that, availability of large amount of biological data information through molecular biology has made drug discovery and development a more complex method. To combat these problems, Quantitative structure-activity relationships (QSAR) emerged as a very useful tool in drug design. QSAR has been applied extensively and successfully over several decades to find predictive models for activity of bioactive agents. QSAR have brought revolution in drug discovery process by thedevelopment of mathematicalrelationships linking chemical structures and pharmacological activity in quantitative manner of series of compounds. Description of the molecular structure, electronic orbital reactivity and the role of structural and steric components has been the subject of mathematical and statistical analysis. Computational drug design method in QSAR is a rapidly growing field which is now a very important component in the discipline of medicinal chemistry. Virtual filtering and screening of combinatorial libraries have recently gained attention as methods complimenting the high-throughput screening and combinatorial chemistry. These chemoinformatic techniques rely heavily on quantitative structure-activity relationships (QSAR) analysis, a field with established methodology and successful history.By characterize a specific aspect of a molecule that is numbers containing structural information derived from the structural representation used for molecules under study called “Molecular descriptors” to find appropriate representations of the molecular structure of drug compoundsto obtain the structure-activity relationships in which these theoretical and computational methods are based, the ability to predict physicochemical, pharmacokinetic and toxicological properties of these leads are becoming increasingly important in reducing the number of expensive methods and late development failures. Thus thereby, QSAR certainly decreases the number of compounds to be synthesized by easing the selection of the most promising candidates. This review seeks to provide a review on role of molecular descriptors in the drug design in QSAR.
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