Quantitative Structure Activity Relationship (QSAR) acts as a function which connects structure of a molecule to different properties. Those properties which show medicinal applications are considered as an activity of a molecule, and, are thus studied within the purview of QSAR. In the present world of global recession, such a technique is able to save money and time. Much of the efforts are now directed towards development of models which predict accurately the properties of a molecule on the basis of their structures. Thus, QSAR models are able to predict biological activities and physico-chemical properties and rationalize the mechanism of action for different chemical reactions. Normally, in order to develop a QSAR, five to ten compounds are necessary for each descriptor as argued by Topliss and Costello (1972) and further mentioned by Schultz et al. (2003c). However, in order to develop robust QSAR, many more compounds are required. With the advent of sophisticated techniques, new software, and, hardware which works on novel algorithms, has made the task of generation of an accurate model within the purview of QSAR simplier. QSAR has its genesis in the pioneering work of Hansch and coworkers (1962, 1964) wherein they used linear multiple regression models to describe the biological property of the com­pounds, which were not synthesized till then. Different parameters such as hydrophobicity, electronic effects and steric properties were utilized to generate a relationship among them and the concentration of the molecule. Free and Wilson (1964) and Fujita et al. (1971) developed non-parameteric methods to define biological activity. Later Linear Solvation Free Energy Relationship (LSFER) was proposed which predicted the properties of a molecule on solute-solvent interactions (Kamlet et al., 1983; Taft et al., 1985). It consists of different solvatochromic descriptors such as cavity/bulk term, polarizability term, and H-bond terms which are used to derive a relationship between overall property of a molecule and different parameters. A good account of the role of QSAR in chemical toxicology was elaborated by Schultz et al. (2003a, 2003b). Other articles also highlight the development of the QSAR methodology and their role in the prediction of bio-toxicity (Gombar et al., 2006; Kar & Roy, 2010; Nantasenamat et al., 2009; Puzyn et al., 2010; Selassie et al., 2010). Hammett (1935, 1937) by employing correlation principles, in 1930s had efficiently shown the effect of structural changes on reaction mechanism of substituted benzoic acid derivatives. It led subsequently to the birth of well known linear free-energy relationship essentially by connecting Gibbs free energy of reaction and Gibbs free energy of activation which in a broader sense established a connection between thermodynamics and kinetics. The concept was further extended by Taft (1956) for aliphatic compounds. This paved the way of strong QSAR models as was evident from the further work carried out by Hansch and coworkers (1993, 1996) wherein they discussed the role of QSAR. The role of QSAR in the study of chemico-biological interactions (Blaney & Hansch, 1990; Debnath et al., 1994; Gao et al., 1999; Hansch et al., 1986, 1989, 1995, 1997, 1998, 2001; Hansen et al., 1995; Langridge & Klein, 1990; Lien et al., 1968;) and also for the drug design (Bajot, 2006; Garg et al., 1999; Hansch et al., 1973, 1995; Kubinyi, 1997; Maddalena, 1998; Norinder, 1998; Voskresensky & Levitsky, 2002; Yang & Huang, 2006;) was also established.