A practical overview of quantitative structure-activity relationship

Chanin Nantasenamat, Department of Clinical Microbiology, Faculty of Medical Technology, Mahidol University
Chartchalerm Isarankura-Na-Ayudhya, Department of Clinical Microbiology, Faculty of Medical Technology, Mahidol University
Thanakorn Naenna, Department of Industrial Engineering, Faculty of Engineering, Mahidol University
Virapong Prachayasittikul, Department of Clinical Microbiology, Faculty of Medical Technology, Mahidol University

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.