Please use this identifier to cite or link to this item:
|Title:||A practical overview of quantitative structure-activity relationship|
|Keywords:||Agricultural and Biological Sciences;Biochemistry, Genetics and Molecular Biology;Pharmacology, Toxicology and Pharmaceutics|
|Citation:||EXCLI Journal. Vol.8, (2009), 74-88|
|Abstract:||Quantitative structure-activity relationship (QSAR) modeling pertains to the construction of predictive models of biological activities as a function of structural and molecular information of a compound library. The concept of QSAR has typically been used for drug discovery and development and has gained wide applicability for correlating molecular information with not only biological activities but also with other physicochemical properties, which has therefore been termed quantitative structure-property relationship (QSPR). Typical molecular parameters that are used to account for electronic properties, hydrophobicity, steric effects, and topology can be determined empirically through experimentation or theoretically via computational chemistry. A given compilation of data sets is then subjected to data pre-processing and data modeling through the use of statistical and/or machine learning techniques. This review aims to cover the essential concepts and techniques that are relevant for performing QSAR/QSPR studies through the use of selected examples from our previous work.|
|Appears in Collections:||Scopus 2006-2010|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.